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Current methods available for assessment of radiolucency and in-between fin (IBF) growth of a glenoid component have not undergone interobserver reliability testing for an all-polyethylene fluted central peg (FCP) glenoid. The purpose of this study was to evaluate anteroposterior radiographs of an FCP glenoid component at ≥48 months comparing commonly used scales to a new method adapted to the FCP. Our hypothesis was that the new method would result in acceptable intra- and interobserver agreement and a more accurate description of radiographic findings. We reviewed ≥48-month follow-up radiographs of patients treated with a primary aTSA using an FCP glenoid. Eighty-three patients were included in the review. Radiographs were evaluated by 5 reviewers using novel IBF radiodensity and radiolucency assessments and the Wirth and Lazarus methods. To assess intraobserver reliability, a subset of 40 images was reviewed. Kappa statistics were calculated to determine intra- and interobserver reliability; correlations were assessed using Pearson correlation. Interobserver agreement (κ score) was as follows: IBF 0.71, radiolucency 0.68, Wirth 0.48, and Lazarus 0.22. Intraobserver agreement ranges were as follows: IBF radiodensity 0.36-0.67, radiolucency 0.55-0.62, Wirth 0.11-0.73, and Lazarus 0.04-0.46. Correlation analysis revealed the following: IBF to Wirth r = 0.93, radiolucency to Lazarus r = 0.92 (P value <.001 for all). This study introduces a radiographic assessment method developed specifically for an FCP glenoid component. Results show high interobserver and acceptable intraobserver reliability for the method presented in this study. The new scales provide a more accurate description of radiographic findings, helping to identify glenoid components that may be at risk for loosening. [ABSTRACT FROM AUTHOR]

• The improved BUS method efficiently incorporates large measurement datasets into Bayesian model updating. • The improved BUS method demonstrates its adaptability to scenarios involving both independent and correlated uncertainties. • The improved BUS method effectively updates spatially variable geotechnical parameters. • The improved BUS method outperforms existing methods in terms of both computational efficiency and accuracy. In engineering practice, Bayesian model updating using field data is often conducted to reduce the substantial inherent epistemic uncertainties in geomaterial properties resulting from complex geological processes. The Bayesian Updating with Subset simulation (BUS) method is commonly employed for this purpose. However, the wealth of field data available for engineers to interpret can lead to challenges associated with the "curse of dimensionality". Specifically, the value of the likelihood function in the BUS method can become extremely small as the volume of field data increases, potentially falling below the accuracy threshold of computer floating-point operations. This undermines both the computational efficiency and accuracy of Bayesian model updating. To effectively address this technical challenge, this paper proposes an improved BUS method developed based on parallel system reliability analysis. Leveraging the Cholesky decomposition-based midpoint method, the total failure domain in the original BUS method, which involves a low acceptance rate, is subdivided into several sub-failure domains with a high acceptance rate. Facilitated with an improved Metropolis-Hastings algorithm, the improved BUS method enables the consideration of a large volume of field data and spatial variability of geomaterial properties in the probabilistic back analysis. The results of an illustrative soil slope, involving spatially variable undrained shear strength, demonstrate that the improved BUS method is effective in simultaneously incorporating a substantial volume of field measurements and observations in the model updating process. Through a comparison with the original BUS method, the improved BUS method is shown to be useful for Bayesian model updating of high-dimensional spatially variable geomaterial properties and slope reliability assessment. [ABSTRACT FROM AUTHOR]

• Subsurface grain misalignment motion of gears leads to fatigue pitting. • Residual stress suppresses crack initiation in carburized gears. • Constructing a gear life prediction model based on the dislocation energy method. • Establish a limit state equation considering the fatigue life of gears. • Grain size and initial crack length are closely related to the reliability of gears. Gears are one of the important components in mechanical products, and their fatigue reliability determines the safety performance of mechanical products. In this paper, the fatigue characteristics of carburized gears under different torque and constant rotational speed conditions are investigated by using a gear contact fatigue testing machine, and combined with the dynamic maximum contact stress distribution on the subsurface of the meshing gears, the very-high cycle fatigue P - S - N curves of carburized gears under a stress ratio of −1 is established. Local stress concentration in the surface or subsurface of carburized gears causes grain dislocation movement under the combined influence of maximum contact and residual stresses, and then causes dislocation pileup and transcrystalline rupture after being hindered by grain boundaries, ultimately leading to pitting and fatigue failure of gears. Based on the dislocation energy method and combined with the fatigue failure mechanism of gears, a life prediction model of gears with good prediction results is established by considering the interaction of factors such as dynamic load, grain size, initial crack length, residual stress, slip band length and width. A fatigue reliability analysis method of gears is established based on the life state equation of gears considering the life prediction model. Further analyses of the influence of rotational speed, grain size, residual stress, slip band width, slip band length and initial crack size on the fatigue life reliability index of the gears resulted in the conclusion that the fatigue reliability of the gears not only decreases with the increase of the above-mentioned parameters, but also shows decreasing trend with the increase of time. This is of significance for evaluating the fatigue reliability of gears under very-high cycle fatigue conditions. [ABSTRACT FROM AUTHOR]

• Maintaining reliability is crucial for district heating systems (DHSs). • State-of-the-art techniques for assessing the DHS' reliability are presented. • Reliability-based asset management strategies are reviewed. • Real-world challenges and potential directions for future research are explored. As district heating systems (DHSs) age, reliability analysis becomes increasingly vital for achieving energy efficiency, meeting sustainability goals, and ensuring the resilience of these urban infrastructures. Traditional reliability and asset management methods are inadequate, as they fail to incorporate the new measurements and varying asset conditions. To address this issue, advanced data-driven approaches that leverage real-time data and cutting-edge analytics are essential. This study thoroughly reviews data-driven techniques utilized for reliability analysis and assessment of DHSs. Additionally, it explores asset management strategies with an emphasis on data-driven reliability-based asset management. The literature review highlights the uniqueness of asset management approaches for each DHS, influenced by factors such as materials, age, environmental factors, and operational variables. The study discusses the existing challenges for reliability-based asset management in DHS and suggests potential research directions. [ABSTRACT FROM AUTHOR]

The global energy crisis, particularly in isolated and remote regions, has increased interest in renewable energy sources (RESs) to meet growing energy demands. Integrating RESs with energy storage systems offers a promising solution to mitigate fluctuations and intermittency, but concerns about cost and reliability remain. This study explores the optimal design of various microgrid configurations, combining photovoltaic (PV), wind turbine (WT), battery energy storage system (BESS), and diesel generator (DG) systems for Najran city, Saudi Arabia, via real-world meteorological and load demand data. The Dwarf Mongoose Optimization Algorithm (DMOA), alongside the salp swarm algorithm (SSA) and whale optimization algorithm (WOA), was applied to minimize the levelized cost of energy (LCOE) while improving system reliability. The results demonstrate that the PV/BESS configuration, although cost-effective with an LCOE of 0.038 USD/kWh, fail to meet reliability constraints with a loss of power supply probability (LPSP) of 0.679. In contrast, the PV, WT, BESS, and DG configurations achieved an LPSP of 1.9 × 10^--8% with an LCOE of 0.199 USD/kWh, offering a robust and reliable solution for the region's energy needs. This paper presents a novel application of the DMOA for optimizing hybrid renewable energy systems, demonstrating its effectiveness in achieving a balance between cost and reliability. This strategy provides a viable approach for sustainable energy planning in similar regions facing energy challenges. • DMOA optimizes Najran City's renewable energy system sizing to reduce LCOE and enhance reliability in hybrid configurations. • Eight hybrid systems (PV, WT, DG, battery) are sized using real Najran data to find the most efficient renewable solution. • Four meta-heuristic optimization algorithms, including DMOA, are compared to identify the best RES configuration. • EMS strategy manages power flow between RESs, supporting system reliability and cost efficiency for Najran's energy needs. [ABSTRACT FROM AUTHOR]

• Reliability assessment of the cable structures with geometrical and environmental imperfections using three different numerical methods. • A comparison of the First Order Reliability Method with the relative entropy-based reliability index. • Statistically-optimized Least Squares Method fittings of the structural responses for cable structures in Ultimate and Serviceability Limit States. The main research problem studied in this work is an uncertain response and reliability assessment of the spatial cable structures due to the environmental stochasticity as well as material and geometrical imperfections. Some popular cable structures are analyzed for this purpose using the Stochastic Finite Element Method (SFEM) implemented with the use of three different techniques, namely the iterative generalized perturbation method, semi-analytical approach as well as the Monte-Carlo simulation. Uncertainty quantification delivered in this study is based on the series of FEM analyses of both static and dynamic structural problems. They enable the Least Squares Method determination of the structural polynomial responses linking extreme stresses and deformations with several uncorrelated uncertainty sources. Reliability assessment, fundamental in durability and Structural Health Monitoring, is completed using a comparison of the First Order Reliability Method (FORM) with probabilistic distance formulated by Bhattacharyya. Input uncertainties are assumed to be Gaussian according to the Maximum Entropy Principle. They have specific expected values following engineering design demands or the provisions of designing codes, whereas their standard deviations do not exceed the 10% level. The methods presented and the results obtained in this study may serve for further reliability analyses of large-scale civil engineering structures completed with both steel cables and also reinforced concrete plates like suspended bridges, for instance. [ABSTRACT FROM AUTHOR]

Electromagnetic relays (EMRs) are intricate micro-electromechanical systems characterized by nonlinear behavior and coupling effects between electromagnetic and mechanical forces. Accurately modeling degradation and assessing reliability are crucial yet challenging tasks for ensuring their safe and efficient operation. Current data-driven methods for degradation modeling and reliability assessment often neglect the known physical knowledge regarding EMRs, leading to inaccuracies in modeling and assessment outcomes when data is incomplete. While physics-informed machine learning (PIML) approaches offer a potential solution, common regression models like Gaussian processes (GP) and long short-term memory (LSTM) suffer from underfitting and overfitting, respectively. To address these issues, we presents a hybrid PIML approach for time-dependent reliability assessment based on the emerging variational autoencoder (VAE) framework. This approach combines the advantages of GP-based methods that enable probabilistic representation with deep neural network-based methods that are more flexible and computationally efficient. Finally, we validate our proposed approach using real-world engineering data, demonstrating its superior accuracy and computational efficiency compared to state-of-the-art methods. • Our VAE-based approach combines physics and data for EMR reliability. • Achieves minimal error, outperforming GP- and LSTM-based methods. • Addresses underfitting and overfitting challenges in alternative techniques. [ABSTRACT FROM AUTHOR]

Estimating the reliability of future energy supply chains is a vital yet complex task driven by environmental and energy security concerns in the context of the ongoing energy transition. This transition necessitates the integration of new technologies and systems into interconnected networks or supply chains. In this context, hydrogen plays a crucial role in the transition to green energy, as it is anticipated a surge in the establishment of "green" hydrogen supply chains (HSC), necessitating the assurance of reliability in meeting international roadmap targets. Technological reliability is typically evaluated by applying quantitative methods to current technologies. For future HSCs, the reliability assessment challenge is related to their prospective nature, with additional uncertainty due to the technologies' interdependencies. When stakeholders rely solely on technology readiness levels, essential aspects of the supply chain are not considered. This work introduces a novel methodology to assess the technological and organizational reliability of future HSCs, contributing to the literature on hydrogen reliability and strategic foresight. It also offers macro-level reliability projections for green HSCs by 2030, integrating input from energy experts and providing valuable insights for the scientific community, academia, and professionals. The proposed methodology's novelty lies in its ability to integrate various nodes of prospective HSCs. The study employs mixed methods, incorporating quantitative (multi-attribute utility theory) and qualitative approaches (horizon scanning). Variables such as capacity, flexibility, infrastructure vulnerability, and consequences of disruption are considered to quantify reliability, with twenty-four metrics included. Data collection employs the perspective of 2030 through a participatory study based on surveys and interviews, drawing insights from twenty-nine international experts associated with various HSCs-related technologies. The methodology is applied to a case study for a green HSC involving solar/wind energy, electrolysis, transportation, storage, and refueling stations. This paper presents the quantitative results, projecting moderate reliability for green HSCs by 2030. Solar HSCs have been considered slightly more reliable than wind HSCs. The interdependence of electrolysis technology and several aspects related to hydrogen transportation are perceived as vital risks affecting the reliability of green HSCs. Having a constant hydrogen supply is seen as a more significant challenge than HSC's response to unexpected interruptions. The research found specific disparities in expert opinions that enriched the data collection process with complementary viewpoints, benefiting from the former's heterogeneous profiles. • The prospective reliability of green hydrogen supply chains (HSCs) is evaluated. • Mixed methods are used to assess reliability (qualitative and quantitative). • Multi-Attribute Utility Theory and Horizon Scanning are used. • Moderate reliability is projected for green HSCs by 2030. • Electrolysis production and transportation presented critical risks. [ABSTRACT FROM AUTHOR]

• Uncertainty of parameters in Huddleston representative stress model is determined by Jackknife method. • Time dependent feature of representative stress is incorporated in creep reliability assessment of components. • Effect of time dependent feature of representative stress on creep reliability assessment is discussed. • Sensitivity analyses of materials parameters on creep rupture life assessment results are conducted. Creep reliability assessment of structural components at elevated temperatures is essential to guarantee the long-term safe operation of the system. Current studies are limited to continuum damage mechanics methods at the material level, while the reliability assessment method for creep design at the component level is rarely reported. In this work, the framework for creep reliability assessment of structural components is extended, where the time dependent feature of the representative stress is included. The effect of the time dependent feature of the representative stress on creep reliability assessment is discussed. Sensitivity analyses of material parameters on creep reliability assessment results are conducted based on the Sobol and Morris global methods. Results indicate that for the same creep design life, the component presents a higher failure probability when the time dependent feature of the representative stress is considered. Parameters D and d in the creep rupture life equation have more significant effects on creep rupture life than other parameters for the case studied. [ABSTRACT FROM AUTHOR]

• An efficient reliability assessment method for passive systems was established and utilized to assess the PRHRS reliability. • A global failure criterion for the whole system was defined rather than a specific criterion for the passive system. • Key parameters were obtained using global sensitivity analysis methods prior to the reliability analysis. • An adaptive PCE model based on bootstrap method was established to solve the obstacle of PCE model in reliability evaluation. Passive safety system is extensively employed in newly designed reactors to increase their inherent safety. However, the driving force is smaller, and the uncertainty of the thermal–hydraulic (T-H) process may potentially result in the inability of system to perform its intended function, which is often known as functional failure. Therefore, the passive systems reliability has received increasing attention. Unfortunately, the assessment of system failure probability utilizing Monte Carlo simulation (MCS) methods necessitates a significant quantity of repeated calculations employing the thermal–hydraulic code, which may be impractical in terms of computational cost. To enhance the assessment calculation efficiency, an adaptive surrogate model is proposed. The bootstrap method was employed to introduce an error estimate into the polynomial chaos expansion (PCE) model, which solves the obstacle of the PCE model in reliability evaluation applications due to the lack of error estimation. The method constructs an active learning approach through error estimation, strategically identifies and selects the best candidate samples, and iteratively updates the initial experimental design. The effectiveness of the method was verified through three benchmark cases, and it was employed for assessing the passive residual heat removal system (PRHRS) reliability within integral-type pressurized water reactor (IPWR200). The results demonstrated that the proposed method significantly diminishes the frequency of invocations to the T-H code and improves computational efficiency to a large extent. Furthermore, it can serve as a valuable guide for the design, operation, and reliability evaluation of the passive system. [ABSTRACT FROM AUTHOR]

• This paper proposes quantum computing models (including device's reliability model and system's reliability model) and an efficient quantum sampling method. • This paper proposes a universal quantum modeling approach for describing the reliability indices LOLP and EENS. • This paper proposes an IQAE-based reliability indices calculation method to achieves accuracy improvement compared to classical methods. Aiming at the reliability assessment of power system, the state sampling process as a critical step requires to generate power system states one by one using classical computer. Due to the quantum superposition, all system states can be generated at once using the quantum computing and then assess the reliability, and thus it will improve the assessment efficiency. However, how to sample system states and assess reliability using the quantum computing theory will be very important. Therefore, this paper proposes a power system reliability assessment technique and its modeling approach based on the quantum computing theory. Firstly, this paper develops a quantum model of power system reliability (including reliability models of electrical device and system) and proposes a new system state sampling method based on the quantum computing theory, which samples all system states at once and improves the sampling efficiency. Secondly, this paper proposes a quantum computing theory based on reliability assessment technique and its modeling approach for the reliability indices: Loss of Load Probability (LOLP) and Expected Energy Not Supplied (EENS). Thirdly, this paper uses an iterative quantum amplitude estimation (IQAE) algorithm to measure the reliability indices for improving the measurement accuracy. Finally, this paper investigates both designed Case and RBTS, and results shows the correctness and effectiveness of reliability assessment of power system based on the quantum computing. [ABSTRACT FROM AUTHOR]

• New approach to optimal placement of u PMUs aiming to improve system reliability. • Only two u PMUs are needed to achieve an acceptable resolution in fault location. • A mathematical formulation to quantify the improvement in the system reliability. • Genetic algorithm and variable neighborhood search solve the u PMU allocation. • Statistical analysis for assessing the performance of the used metaheuristic. Due to the dynamic nature of modern distribution systems, the deployment of micro-phasor measurement units (u PMU) is becoming increasingly common among utilities to improve system monitoring and reliability. However, given their high investment costs, deploying a large number of these devices becomes unfeasible. Hence, unlike other approaches found in the literature that focus on observability criteria, this work presents an algorithm for optimal placement of u PMUs aimed at improving distribution system reliability. The algorithm defines the optimal number and location of the u PMUs through an objective function based on the resolution of a fault location technique that works in conjunction with pseudo-measurements to successfully locate a contingency. The meta-heuristics Genetic Algorithm and Reduced Variable Neighborhood Search are employed to address this problem. The proposed method has been validated on a three-phase 39-bus distribution system and a real distribution feeder with 962 buses from an Ecuadorian electric distribution utility. The results obtained confirm the effectiveness of the method, as with the deployment of only two u PMUs, the energy not supplied decreases by 13.84 % and 24.96 % for the 39-bus and 962-bus systems, respectively. Moreover, in the 962-bus system, the System Average Interruption Duration Index (SAIDI) is reduced by 20.36 %. [ABSTRACT FROM AUTHOR]

Australia has significant potential for the development of offshore renewable energy systems (ORES), and it can play an essential role in the global energy transition. The planning, design, installation, operation, and end-of-life management of ORES present substantial challenges in terms of the reliability of systems and the safety of operations. This paper focuses on identifying the gaps and challenges related to the structural integrity of ORES, highlighting potential areas for technological and managerial improvements. The paper investigates Australia's existing policies and regulations, identifies their shortcomings, and provides recommendations for their advancement. Key recommendations include implementing robust regulations, enhancing site-specific knowledge, adopting structural health monitoring (SHM) from the design phase, and fostering industry collaboration to accelerate ORES development and sustainability. The findings reveal high failure rates in ORES components, attributed to harsh marine conditions, material degradation, and extreme weather, underscoring the need for standardized protection and preventive measures. Integrating climate change impacts into dynamic risk assessments is crucial for accurate failure and consequent analyses. The study advocates learning from other engineering sectors to bridge existing gaps and align with sustainable offshore development goals. These recommendations aim to assist policymakers, regulators, and technology developers in realising safer and more sustainable ORES for Australia. [Display omitted] • A review of knowledge gaps and challenges in ORES safety and reliability. • Analyze limitations in existing international and Australian standards; recommend future R&D. • Overview ORES risk and reliability, focusing on structural failures. • Recommend measures to enhance ORES safety and sustainability. [ABSTRACT FROM AUTHOR]

Forest map products are widely used and have taken benefit from progresses in the multisource forest inventory approaches, which are meant to improve the precision of forest inventory estimates at high spatial resolution. However, estimating errors of pixel-wise predictions remains difficult, and reconciling statistical outcomes with map products is still an open and important question. We address this problem using an original approach relying on a model-based inference framework and k -nearest neighbours (k -NN) models to produce pixel-wise estimations and related quality assessment. Our approach takes advantage of the resampling properties of a model-based estimator and combines it with geometrical convex-hull models to measure respectively the precision and accuracy of pixel predictions. A measure of pixel reliability was obtained by combining precision and accuracy. The study was carried out over a 7,694 km2 area dominated by structurally complex broadleaved forests in centre of France. The targeted forest attributes were growing stock volume, basal area and growing stock volume increment. A total of 819 national forest inventory plots were combined with auxiliary data extracted from a forest map, Landsat 8 images, and 3D point clouds from both airborne laser scanning and digital aerial photogrammetry. k -NN models were built independently for both 3D data sources. Both selected models included 5 auxiliary variables, and were generated using 5 neighbours, and most similar neighbours distance measure. The models showed relative root mean square error ranging from 35.7% (basal area, digital aerial photogrammetry) in calibration to 63.4% (growing stock volume increment, airborne laser scanning) in the validation set. At pixel level, we found that a minimum of 86.4% of the predictions were of high precision as their bootstrapped coefficient of variation fall below calibration's relative root mean square error. The amount of extrapolation varied from 4.3% (digital aerial photogrammetry) to 6.3% (airborne laser scanning). A relationship was found between extrapolation and k -NN distance, opening new opportunities to correct extrapolation errors. At the population level, airborne laser scanning and digital aerial photogrammetry performed similarly, offering the possibility to use digital aerial photogrammetry for monitoring purposes. The proposed method provided consistent estimates of forest attributes and maps, and also provided spatially explicit information about pixel predictions in terms of precision, accuracy and reliability. The method therefore produced high resolution outputs, significant for either decision making or forest management purposes. [ABSTRACT FROM AUTHOR]

The high-temperature rotating structures (HTRS), e.g., steam turbine rotors, often operate in extremely harsh environments with a flexible load condition during peak shaving of power system. In this work, a damage-driven framework for reliability assessment is developed in terms of the cumulative damage-damage threshold interference (CD-DT) principle, in which the cumulative damage and damage threshold are regarded as two random parameters to address uncertainties. The CD-DT principle is founded on the engineering damage theory and incorporates physics-of-failure into the probabilistic modeling of high-temperature structural reliability. Probabilistic damage analysis, correlation analysis of weak sites, system-level reliability analysis, and sensitivity analysis have been encompassed in this framework. Three numerical examples are used to verify the effectiveness and applicability of the proposed framework. Application to steam turbine rotor involving multiple weak sites with multi-damage modes illustrate the implementation procedures of the framework. Results show that the reliability-based design life of rotor decreases with the increases of start-stop frequency, the implementation of a two-shift operation would pose a threat to meeting the safety requirement of a 30-year design life. Furthermore, sensitivity analysis highlights the critical influences of initial rotor temperature and speed rising rate on rotor reliability, providing insights for operational maintenance and reliability optimization. [ABSTRACT FROM AUTHOR]

• Considering degradation effects in solder joint reliability via multistate modeling. • Considering multiple failure mechanisms for comprehensive reliability study. • Solder joint failure mechanisms analysis based on physics of failure. Investigating the impact of solder joint degradation in power semiconductors is crucial given their widespread use. Solder joints, being the weakest link, are prone to multiple failure mechanisms, exacerbating degradation under normal operation. This paper presents a novel approach for assessing solder joint degradation, particularly in electric vehicles, where severe thermal and vibration stresses are prevalent. Our method categorizes solder joint conditions into discrete states, from fully perfect to fully damaged, integrating both thermal and vibration-induced failures. By considering cumulative effects, our approach yields more precise reliability estimates. Moreover, it offers a versatile framework applicable across various failure scenarios and readily deployable in the design phase. [ABSTRACT FROM AUTHOR]

• The part of the failure criteria and uncertainty design parameter has been rewritten • The part of the results has been redone and the computational time has been supplemented. • The part of the conclusion has been rewritten. • The test of the improved kriging model of the passive residual system has been redone. Accurately quantify the failure probability of passive safety systems can promote the application of passive safety technology in nuclear power plants. However, the complex and variable marine conditions introduce more uncertainty to the operation of passive safety systems, posing additional challenges to the reliability analysis of passive safety systems under marine conditions. Furthermore, huge calculation cost is one of the problems in the reliability assessment of passive safety system. In response to these limitations of the reliability assessment, a reliability analysis method for passive safety systems is proposed based on the improved Kriging model and SORM method in this paper. It is applied to the reliability analysis of the passive residual heat removal systems of the marine IPWR200. The calculation results show that the failure probability of the passive residual heat removal systems of the IPWR200 under marine conditions is approximately 2.9308 × 10-4. Compared to the Monte Carlo method, the method which coupling with improved Kriging and SORM can reduce the computational cost of reliability analysis while ensuring the accuracy of the results. The sensitivity analysis result show that the marine movements are the major uncertainty parameters which is significant for system performance. The marine movements significantly impact the driving force of natural circulation and generate a periodic additional force which lead to the periodic oscillation of natural circulation. [ABSTRACT FROM AUTHOR]

Design failure modes, effects, and criticality analysis (d-FMECA) 2 2 The term FMEA and FMECA are used interchangeably throughout this paper. The user has the option in the interface to choose either FMEA or FMECA based on their desired application. is a bottom-up, semi-quantitative risk assessment approach that is used by reliability engineers across all industries (nuclear, chemical, environmental, pharmaceuticals, aerospace, etc.) for identifying the effects of postulated components failure modes such as solenoid-operated valves (SOV), motor-operated valves (MOV), controllers, pumps, sensors of various types, printed circuit boards (PCBs). This research aims to develop a novel AI-augmented tool that guides, in real-time, the risk-analyst to a host of potential failure modes and their effects for each component contained in a bigger system. Through a user-friendly graphical interface and a robust statistical modeling backend, the AI-driven tool streamlines the risk assessment process by prompting the risk analyst to input a system's name and subsequently generate an extensive array of failure modes and associated effects for each constituent component within the system. This AI-augmented tool allows the user to select either a simplified d-FMEA or a detailed d-FMECA for the system under investigation. This novel AI-driven tool offers significant effort and time savings in conducting d-FMECA, which is known to be a labor-intensive engineering task. In addition, this tool can be used for training risk and reliability professionals. • Developed a novel AI-augmented tool to replace conventional d-FMEA approach. • Designed a user-friendly graphical interface & robust statistical modeling backend. • This AI-driven tool offers significant effort & time savings in conducting d-FMECA. • Help train junior reliability engineers to effectively conduct d-FMEA. [ABSTRACT FROM AUTHOR]

Data-driven approaches on large-amount labelled data have been actively implemented for reliability assessment of rotating machine, i.e., with limited labelled data samples. Recently, the study of large language models (LLMs) has demonstrated outstanding performance in the field of natural language processing. Inspired by the LLM for addressing sequential data, this article investigates the implementation of LLM under partial information for reliability assessment of rotating machine. A novel LLM-based two-stage framework, called ParInfoGPT, is proposed by integrating a self-supervised reconstruction network and a weakly supervised classification network. Additionally, a mutual information (MI)-based informative masking strategy for pre-training and a parallel side-adapter (PSA) for fine-tuning are designed to effectively learn the proposed framework. Experiments are systematically conducted and evaluated on two real-world datasets of rotating machine. The experimental results of the proposed methodology demonstrate its superior performance on fault diagnosis under partial information for reliability assessment. • A large language model-based diagnosis framework with two stages is established. • A mutual information-based masking strategy is designed for a reconstruction stage. • A parallel side-adapter for side-tuning is proposed for a classification stage. • A reliability assessment methodology is developed under partial information. [ABSTRACT FROM AUTHOR]

Probabilistic methods are frequently employed in the reliability assessment of building air-conditioning design loads to quantify the uncertainty associated with building thermal parameters. However, its implementation requires some assumptions because of inadequate data in the early stages of design. These assumptions cause the uncertainty quantification results of the building thermal parameters to differ from reality, which may lead to a significant overestimation or underestimate of reliability. Therefore, an evidence theory-based method was proposed to quantify the uncertainty of the building thermal parameters. In this method, evidence theory was employed instead of probability theory because the former could make full use of small sample information to achieve objective uncertainty quantification. Taking an office building in Tianjin, China, as an example, the new method was tested, and its effectiveness was verified. The results showed that the uncertainty quantification results of the building thermal parameters based on the new method included those of the benchmark method. Under small sample conditions, the reliability assessment results based on the new method were closer to those of the benchmark compared with the traditional method. [ABSTRACT FROM AUTHOR]

Wales is committed to meeting the Net-zero emissions target by 2050. To meet this challenge unprecedented changes in the energy system are required in South Wales. There are various pathways to achieve net-zero emissions in South Wales. These pathways are usually compared based on their costs. However, energy supply reliability assessment is required to determine the security of these scenario pathways. A probabilistic dynamic reliability assessment framework is proposed, which combines the Improved Universal Generating Function and the Improved Fisher optimal algorithm. This technique reduces the computational burden of reliability assessment by 95% with a similar accuracy compared with Monte Carlo Simulations. The impacts and sensitivities of energy sources and technologies on supply reliability in High Electrification and High Hydrogen scenarios are measured. As the penetration level of local renewables increases five-fold in 2050 compared with 2020, Loss of Load Expectation increases from 3 to 10–12 h/year in High Electrification and High Hydrogen scenarios. In summer, the reliability of energy supply is more sensitive to changes in the assumptions on failure probabilities of energy conversion devices, whereas the system's reliability in winter is mainly impacted by the availability of imported energy to South Wales. • PMFs of variables obtained from historical data are used in the Improved UGF. • A systematic dynamic reliability assessment framework is developed. • Energy supply reliability across distinct net-zero energy futures is investigated. [ABSTRACT FROM AUTHOR]

Because the dependence, competition, correlation, and other complex interactions among the components and between system and environment, the complex system is difficult to be modeled directly. As an important spacecraft system, satellite needs to meet the high reliability demand. However, the increasingly complex satellite system structure and complex space environment interference make the on-orbit satellite failure inevitable. Modeling and evaluation of satellite conditions can identify potential problems of key system components in time. Firstly, in order to accurately describe the system components and measure the reliability, the correlative damage coefficient is introduced to describe the component degradation process, and the component reliability model of the satellite attitude control system is established. Then, to solve the problem of large state space and changeable condition of system reliability model, the concept of tangent order pair is introduced into the multi-state dynamic fault tree of system. The depth-first search algorithm is used to sort the underlying event components of the fault tree, and the multi-state multi-value decision graph model of different state events is constructed. The minimum cut set of the model is obtained to evaluate the system state. The experimental results show that the proposed method can effectively simulate the system situation under different fault modes, effectively reduce the state space of the system reliability model, and improve the convergence and accuracy of the system fault probability evaluation. [ABSTRACT FROM AUTHOR]

• A novel comprehensive reliability assessment methodology is proposed which can handle both temporary and permanent faults in distribution networks. • A novel component model is developed considering low voltage ride-through control, fault types and protection actions. • Fault consequence analysis methodology that proposes differential modeling thinking for temporary and permanent faults. Upon the large-scale integration of inverter-interfaced distributed generator (IIDG) into the grid, random faults in the distribution network can lead to momentary and sustained interruptions, significantly impacting system reliability. Although reliability methods have been widely used in distribution network adequacy assessment, using probabilistic method for reliability assessment including system dynamic security need to be investigated. To address this issue, a new method is designed to assess the reliability of distribution network using sequential Monte Carlo simulation. Firstly, the IIDG low-voltage ride-through (LVRT) control strategy is formulated, and the off-grid probability model for IIDG is developed based on the Gaussian distribution. Secondly, the component model is defined to consider the impact of random faults on power quality in security assessment. Following the fault tree analysis method, a protection action probability model was formulated to assess the failure probability of line current differential protection, IIDG anti-islanding protection, and reclosing protection. Finally, the method for momentary fault consequence analysis, based on depth-first search (DFS), and the method for sustained fault consequence analysis, based on the mixed-integer linear programming model, are developed. The study establishes a comprehensive probability reliability assessment framework. The validity of the method is demonstrated on the IEEE RBTS BUS6 F4 system, indicating good scalability. [ABSTRACT FROM AUTHOR]

Nowadays due to increasing energy demand in the world, the use of energy systems with maximum efficiency is unavoidable. Thermal integration of the systems decreases the number of equipment employed and also raises the efficiency of the hybrid configuration. In the present study, an innovative hybrid system for cogeneration of power and desalinated water including organic Rankine unit, carbon dioxide power plant, Kalina power cycle based on the seawater temperature difference, and multi-effect desalination system is developed. Part of the heat required by the system is provided by solar flat plate collectors. The innovative hybrid system produces 849.5 MW power and 198.5 kg/s potable water. Exergy investigation of the system indicates that the most exergy destruction in the whole system belongs to the combustion chamber and heat exchangers, each of which is 46.60% and 34.91% of the total exergy destruction, respectively, which shows that more than 80% of exergy destruction is related to these two parts. The exergy efficiency of the whole system is 44.81%. Through the pinch method, the heat exchanger network related to the multi-stream heat exchangers of the hybrid system is extracted. The effect of air /fuel ratio (inlet to the combustion chamber) on system performance in the parametric analysis is examined. One of the main results is a 12.69% improvement compared to the initial state of total electrical efficiency of the structure in case of increasing the fuel input to the combustion chamber up to 62.00 kg/s. By system reliability analysis and examining different modes of failure and repair rates of different sections of the system, the probability of the structure operation in different cases is extracted. [Display omitted] [ABSTRACT FROM AUTHOR]

The reliability assessment of train control and management system (TCMS) is essential for the condition monitoring of high-speed train. Different from other general complex systems, the TCMS has the characteristics of multi-system unit, strong coupling and multiple factors. Considering the special system operating environment and high safety requirements of high-speed train. In this paper, for the reliability assessment of TCMS, we propose a new quantitative model based on the evidential reasoning rule and covariance matrix adaptation evolution strategy algorithm, the proposed model offers the following advantages: it has a strong modeling capability for the TCMS reliability, it has an interpretable model assessment process, it can describe the assessment result under probabilistic uncertainty and ignorance uncertainty, and it possesses considerable robustness. To make the model interpretable, an assessment hierarchy is established for the TCMS; to improve model robustness, weights interval is applied to replace the trained weights as the model weights. Several traditional methods are compared with the proposed model to demonstrate its performance, the results show that the proposed model has a better training accuracy. Moreover, a case study is conducted to verify the model's functional feasibility. • An assessment model based on ER rule and CMA-ES is established for TCMS. • Compared with traditional methods, the model has stronger modeling ability. • The model has an interpretable assessment process, and possesses robustness. • The model can describes the assessment result under uncertainty. • A dataset extension method is proposed to enhance the scale of dataset. • The weights interval is applied to replace the optimal weights offered by CMA-ES. [ABSTRACT FROM AUTHOR]

Reliability analysis of IESs (Integrated Energy System) is complicated because of the complexity of system topology and dynamics and different kinds of uncertainties. Reliability is often calculated based on statistic methods, which always focus on historical performances and neglect the importance of their dynamics and structure. To overcome this problem, in this paper, a systematic framework for dynamically analysing the real-time reliability of IESs is proposed by integrating different machine learning methods and statistics. Firstly, the bootstrap-based Extreme Learning Machine is developed to forecast the conditional probability distributions of the productions of renewable energies and the energy consumptions. Then, the dynamic behaviour of IESs is simulated based on a stacked auto-encoder model, instead of using traditional mechanism-based simulation models, for improving computational efficiency. Besides, the variables representing the transient properties of natural gas pipeline networks, such as delivery pressures and flow rates, are taken as the indicators for quantifying the energy supply security in natural gas pipeline networks. The time-dependent relationships among these indicators and their statistic correlations are modelled for improving the effectiveness of the analysis results. Finally, the reliability assessment is performed by estimating the probability distribution of each functional state of the target IES. A case study of a realistic bi-directional IES is carried out to demonstrate the effectiveness of the proposed method. The results show that the method is able to effectively evaluate the reliability of IESs, which can provide useful information for system operation and management. • A novel framework for predicting the real-time reliability of supply in IESs. • Prediction Intervals of variables are used in IES's reliability assessment. • A data-driven model is proposed to improve the computational burden. • Multi-modal system functional states are used to analyse the system' functionality. [ABSTRACT FROM AUTHOR]

Power system reliability evaluation has been conducted based on steady-state analysis approaches such as optimal power flow calculations with minimum load curtailments. These approaches assume that a power system would return to a stable operating state after a contingency (faults, short circuits, etc.), i.e., power system dynamics are not considered. Although this assumption has been widely accepted for conventional power systems, microgrids are more vulnerable to large disturbances than conventional power systems are. In other words, the likelihood of an unstable transition from pre-event to post-event conditions is higher in the case of microgrids than in the case of conventional power grids. Therefore, it has become important to consider both generation adequacy and transient stability in microgrid reliability evaluation. This paper develops an integrated transient stability and reliability assessment approach for microgrids to capture both inadequacy and instability conditions. A Lyapunov function-based approach is developed to determine the stability region for each contingency in calculating the reliability indices. Also, a linearized AC optimal power flow model is developed for composite system reliability evaluation. This paper also provides a set of indices to quantify the impact of transient instability on the reliability of microgrids. The proposed approach is applied to the IEEE 33-bus system in the islanded mode using sedumi and YALMIP optimizers in MATLAB. The results show that 29.19% of the contingencies for which the microgrid is deemed reliable based on steady-state analyses causes unstable conditions, which justifies the need for integrating steady-state and transient conditions in microgrid reliability evaluation. • Integrated framework for microgrid reliability and transient stability assessment. • New transient energy function for large-scale microgrid stability assessment. • Introduced stability-reliability indices for microgrid analysis. [ABSTRACT FROM AUTHOR]

• An optimal load shedding model for IEDCSs with detailed topology and transmission energy loss is established. • The evaluation method for reliability indices is improved considering the thermal inertia characteristics and the transportation delay. • The sequential MCS is employed to describe the time-varying characteristics of load and the fault and repair characteristics of equipment. The tight interactions between energy sectors increase the risk of affecting integrated energy systems (IESs); therefore, reliability assessment is important for identifying vulnerability and improving security in IESs. In the literature, the network topology, transmission loss, and distinguishing thermal dynamics in IESs are seldom addressed for reliability assessment, rendering the results unreliable. To bridge this gap, an integrated electricity and district cooling model is formulated for IESs that involves elaborate network topologies and transmission losses to reflect their impacts on reliability characteristics. In addition, the evaluation of reliability indices for district cooling systems is improved by considering the thermal inertia characteristics of buildings and the transportation delay of circulating water to characterize the reliability levels of district cooling systems more accurately. Numerical results using a 12-node case validate the improved accuracy of the proposed reliability assessment method and reveal the potential of district cooling systems for enhancing the reliability of power systems. [ABSTRACT FROM AUTHOR]

• A method to simulate wind fields is proposed considering probabilistic spectra. • The correlated spectral parameters are decoupled in the proposed method. • The proposed method can combine with the PDEM to assess structural reliability. • The reliability of a tower crane is compared between deterministic and probabilistic spectra. The need for wind-sensitive super-high tower cranes during the construction of high-rise structures has been growing, which makes it critical to assess the wind-resistant reliability of cranes. The accuracy of assessment is dominated by the refined simulation of real stochastic wind fields, which is usually based on the deterministic turbulent spectra including fixed spectral parameters. However, it might cause significant errors to estimate extreme responses of structures. Therefore, a simulation method for stochastic wind fields considering the randomness and correlation of turbulent spectral parameters is proposed in this study. The random functions of correlated spectral parameters are constructed based on several independent elementary random variables. A novel simulation method for stochastic wind fields involving the established random turbulent spectral functions is utilized to generate fluctuating wind samples at the Ma'anshan Yangtze River (MYR) Bridge site. In combination with the probability density evolution method, the stochastic response analysis and reliability assessment of wind-induced vibration of the crane are addressed. Results prove the effectiveness of the proposed approach in depicting probabilistic properties of real wind fields. Meanwhile, the wind-resistant comfort reliability of the tower crane is higher when considering the randomness and correlation of turbulent spectral parameters. [ABSTRACT FROM AUTHOR]

• To reduce the impact of outliers on the reliability assessment of electric metering devices (EMDs), a novel bi-directional (BD) outlier detection approach is proposed, which employs MCD robust analysis and Thompson tau to identify horizontal and longitudinal outliers in the measurement error (ME) observations, respectively. The BD detection method can effectively avoid misdetections and omissions. • Then, a Wiene-based multi-stress fusion nonlinear degradation (MFND) model is constructed to establish the relationship between multiple stresses and the ME data. It can provide future input values for environmental stresses according to the characteristics of the environmental stress fusion term. The fluctuation trend of ME under time-varying stresses can be effectively tracked using MFND, and its parameters can be greatly interpreted via affine invariant ensemble MCMC (GWMCMC). • A reliability assessment framework that can characterize the dynamic performance of EMDs is proposed by combining the BD outlier detection method and the MFND model, which can accurately predict the trend of ME data. Then, the reliability of batch devices is assessed based on the predicted pseudo-life, and recommendations are made for maintenance intervals. Finally, we conduct extensive experimental tests using ME data from smart meters, and the results demonstrate that our framework possesses exceptional predictive capability in real-world harsh environments, affirming its effectiveness. Reliability assessment for electric metering devices (EMDs), which includes environmental stress analysis, measurement error (ME) prediction, and reliability estimation, can be utilized for predictive instrument maintenance, especially under harsh environmental stresses. Nevertheless, the actual evaluation process is limited by data noise and the unavailability of future environmental inputs for the degradation model. To this end, we first extract the main environmental factors affecting ME and then fuse them using the weighted principal component analysis (WPCA) method to provide future environmental input values for the prediction model. Next, a bi-directional (BD) outlier detection method based on MCD robust analysis and the Thompson tau method is proposed to detect outliers from horizontal and longitudinal perspectives. Then, an ME prediction method called multi-stress fusion nonlinear degradation (MFND) is put forward, which considers the cyclic variation of ME over time and the effect of environmental stresses on ME. Real-world datasets from a high-dry-hot area manifest that our proposed BD-based MFND reliability assessment framework enjoys pleasurable prediction performance. Compared to some well- known methods, our framework excels in outlier detection and ME prediction. [ABSTRACT FROM AUTHOR]

The high-temperature superheater outlet header (Outlet Header) in ultra-supercritical (USC) thermal power plants is subjected to high temperatures and pressures, which increases the risk of creep failure. To assess the structural reliability of the Outlet Header, it is necessary to consider the impact of uncertainty factors. Furthermore, the diverse operating conditions make reliability assessment inconvenient. This study evaluates the creep life reliability of the Outlet Header based on material uncertainty and simplifies the assessment process using machine learning methods. Considering the scatter of creep rupture data, the uncertainty of material constants in the Larson-Miller (LM) model is quantified by randomly sampling a specific number of creep rupture life data. Based on the results of uncertainty quantification and finite element analysis, the distribution of the Outlet Header's creep life is obtained to calculate its reliability under design life. Machine learning is employed to assist in the reliability assessment of creep life under different operating conditions of Outlet Header. The results indicate that Artificial Neural Network (ANN) demonstrates good performance in this study, and an assessment diagram based on the ANN has been constructed. This approach provides a practical solution for assessing the reliability of high-temperature components in engineering. • Establishes a P92 steel creep rupture dataset and quantifies the uncertainty of Larson-Miller material constants. • Conducts reliability assessment of the creep life of the high-temperature superheater outlet header. • Utilizes ANN to construct reliability assessment diagram under different operating conditions. [ABSTRACT FROM AUTHOR]

Guided wave-based structural health monitoring is an attractive option for detecting structural defects in an automated manner. In this work, we focus on the task of damage localization. Deep learning methods have been shown to have superior performance for damage localization. Yet, environmental variations introduce uncertainty in the system and reduce its reliability. For this reason, it is crucial to assess the reliability of estimates taken from structural health monitoring systems. In this work, we estimate the localization reliability from a single snapshot of sparse array guided wave measurements instead of reporting values averaged over an entire set of test measurements. The assessment strategy can be added to any deep learning localization model and produces both a localization and uncertainty estimate. The deep learning model is trained using only guided wave simulations. We assess the uncertainty using both simulated and experimental data with temperature variations. Multiple deep learning-based uncertainty quantification methods are analyzed. Results demonstrate correlations between uncertainty, temperature variations, and the presence of synthetic damage. We also compare with reliability derived from delay-and-sum localization. We find that a deep ensemble learning strategy provides the most reliable damage localization and uncertainty quantification. [ABSTRACT FROM AUTHOR]

• A new heuristic ensemble strategy is proposed. • Parallel enrichment strategy is developed by pseudo-improved U learning function. • Ensemble of metamodels combined with hypersphere sampling for rare events. • The proposed method is used for reliability assessment of aircraft lock mechanism. In practical engineering, especially in aeronautical engineering, the failure probability is extremely rare due to the incorporation of safety factors in the mechanical design phase. Consequently, a significant challenge is to assess the reliability of mechanical products with implicit functions and rare failure events. To address this issue, this work presents a parallel adaptive ensemble of metamodels (EM) coupled with hypersphere sampling strategy to improve the accuracy and efficiency of reliability analysis. The proposed method consists of three main features. First, a new heuristic ensemble strategy is proposed to provide a powerful and robust metamodel. Second, a n-dimensional uniform sampling technique with better space-filling ability is taken to improve efficiency, which leads to a decrease in the extensive sample size required to capture rare failure events. Third, an effective parallel enrichment strategy is developed by the proposed pseudo-improved U learning function. When parallel computation is possible, the proposed method can select a batch of informative updated points simultaneously to update the EM. Three numerical examples and a planar ten-bar structure are presented to demonstrate the accuracy and efficiency of the proposed method. This method is also applied to the reliability assessment of the aircraft lock mechanism. [ABSTRACT FROM AUTHOR]

• Information entropy is first used for Kriging-based system reliability assessment. • The prediction uncertainty of the system state is quantified by information entropy. • A learning function is proposed using the prediction uncertainty of the system state. • Sample with the largest uncertainty get more attention for model reconstruction. Structural reliability assessment is a popular topic in engineering problems, particularly for the larger and more complex systems with implicit performance functions, also called black-box problems. The reliability assessment for a black-box problem must continuously be computed using simulation models, such as the finite element model, a highly time-consuming process with a high computational cost. The adaptive Kriging has gained considerable attention over the past decade. The Kriging-based reliability assessment method reduces the computational cost to a great extent, on the premise of ensuring the accuracy of reliability assessment. However, many of the currently published system reliability assessment methods, construct adaptive Kriging models by reducing the probability of incorrect prediction of the system state, and do not make full use of the uncertainty of the system state prediction information. To this end, a new Kriging-based method for structural system reliability assessment is proposed in this study. First, the probabilities of incorrect or correct system state predictions were understood from the perspective of information entropy. Second, an active learning strategy is proposed based on information entropy theory. Finally, the advantages of the proposed method are demonstrated and highlighted through several numerical examples. The results show that the proposed method achieves a good balance between the accuracy and computational cost, and the numerical magnitude effect does not affect the computational cost. Moreover, this is an effective method for assessing the reliability of complex systems. [ABSTRACT FROM AUTHOR]

Reliability assessment for renewables-dominated strong power systems presents significant challenges, primarily due to the overwhelming computational demands posed by large-scale renewable generation and energy storage. In response to the limitations and lack of scalability in traditional methods, this study introduces a novel four-stage fast reliability assessment framework tailored for renewables-dominated strong power systems. First and foremost, the pre-dispatch and temporal decoupling of energy storage serve as vital foundations for the fast assessment framework. Following the pre-dispatch model, an adaptive scenario clustering technique is proposed to eliminate redundant scenarios while retaining extreme ones. Consequently, a quick verification process for contingencies is established, employing DC optimal power flow in the worst-case scenario to identify potential load shedding. Then, all contingencies are filtered to only effective ones for reliability indices computation. Finally, a modified reliability calculation model with linear AC power flow is built using the filtered contingency set and reduced scenarios. The efficacy of this approach is validated through four numerical experiments, demonstrating impressive efficiency and computation feasibility. For accuracy, the proposed method shows a 3.7% average error in a modified IEEE RTS system, and for efficiency, the proposed method finishes N-2 analysis in 424 s for a 62-bus-212-line case as well as 4924 s for a 200-bus-490-line case. • A four-stage fast reliability assessment is developed. • Energy storage is pre-dispatched and temporally decoupled. • Scenarios are adaptively reduced with extremes retained. • Contingency filtering process examines effective ones. • Capable and efficient for large-scale strong grids. [ABSTRACT FROM AUTHOR]

Managed Pressure Drilling (MPD) system is widely used in the deepwater drilling operation. Reliability assessment plays a critical role in the MPD system in the management of drilling operation risk and the prevention of blowouts. However, the reliability assessment of the MPD system is challenged due to its sequential operations and multiple processes. Consequently, the present work proposes a sequence-based dynamic reliability assessment method, which focuses on the dynamic modeling of sequential operations for the MPD system by integrating GO-FLOW and dynamic Bayesian Network (DBN). GO-FLOW models are firstly used to define the time interaction between multiple phases for complex systems. A sequence-based mapping method is also proposed for the DBN to construct the reliability model of the MPD system throughout the entire drilling cycle. In the end, the case study analyzed by the proposed framework indicates that the reliability of the MPD system decreases with increasing drilling depth, and the reliability of "tripping in" is highest among four different phases, while the "drilling process" is the lowest. The method provides an important technique that can be implemented with online condition monitoring tools to assess and monitor the reliability of the MPD operation in real-time. [ABSTRACT FROM AUTHOR]

Reliability assessment of Photovoltaic (PV) modules is very crucial to increase the service lifetime of PV systems. This study assesses the degradation rate and reliability of PV modules operated for twelve years under the tropical climatic condition in sub-Saharan. For this purpose, various characterization techniques, namely visual inspection, infrared (IR) thermography assessment, and current-voltage (I-V) characterization, have been employed to evaluate the performance of PV modules. Moreover, the functioning of bypass diodes has been tested under partial shading situations. The results demonstrate that after twelve years of outdoor operation, the short-circuit current (I sc) of modules have been degraded up to 16.4% with an average decrease of 11.7% compared to the nameplate values. The open-circuit voltages (V oc) were reduced from 11.4% to 17.1% with a mean of 14.8%. The decline in Fill Factor (FF) of the modules ranges from 11.3% to 24.2%, and the losses of power output were between 34.5% and 41.4%. Moreover, the visual and thermography assessment reveals that the PV modules are severely affected by various failures such as EVA browning, cell interconnects ribbons browning and the corrosion of solder bonds. The results show that averagely, the FF is the most significant factor influencing the loss in the power output of the modules. • Degradation of PV modules under hot, humid tropical climate for 12 years assessed. • PV module characteristics normalized at STC using derived translation equations. • Influence of module parameters on degradation evaluated using regression analysis. • The average degradation of nominal power is 3.19%/year. • The combination of Isc-Voc had the most impact on the variation of nominal power. [ABSTRACT FROM AUTHOR]

System reliability assessment plays a crucial role in making maintenance decisions and reducing hazard frequencies. Although many engineering methods can effectively evaluate the process reliability, most of them are often unreasonable for closed-loop systems because of the combination of closed-loop structures, maintenance characteristics, and dynamic failure mechanisms. Also, uncertainties generally exist in the reliability assessment due to the insufficient reliability data and expert knowledge. Therefore, an integrated approach is proposed in present works to assess the dynamic reliability of repairable closed-loop systems with the consideration of uncertainties. Firstly, Bayesian inference and fuzzy theorem are developed to characterize system uncertainties and estimate lifetime parameters of components. After that, a closed-loop probabilistic reliability assessment (CPRA) method is proposed for the dynamic reliability assessment of closed-loop systems by integrating cyclic Bayesian network modeling and dynamic Bayesian network solving. Besides, a novel non-probabilistic reliability assessment (NPRA) approach based on the probabilistic method and Monte Carlo simulation is presented to make maintenance decisions for repairable systems. Finally, an application of reliability assessment for the offshore crude oil separation system is introduced to verify the proposed methods. • Fuzzy-based parameter estimation is proposed to quantify epistemic uncertainty. • CBN is developed for dynamic reliability modeling of closed-loop systems. • NPRA is proposed based on CBN and MCS for maintenance decision-making. [ABSTRACT FROM AUTHOR]

The hydraulic system is one of the most critical subsystems of wind turbines. It is used to reset the aerodynamic brakes. Because of this, the reliability of the hydraulic system is important to the functioning of the entire wind turbine. To realistically assess the reliability of the hydraulic system, we propose in this article the load-sharing based reliability model using survival signature to conduct system reliability assessment. In addition, due to the uncertainty of the failure rates, it is difficult to conduct accurate reliability analysis. The Markov-based fuzzy dynamic fault tree analysis method is developed to solve this issue for reliability modeling considering dynamic failure characteristics. Following this, we explore the reliability importance and the reliability sensitivity of redundant components. The relative importance of the components with respect to the system reliability is evaluated and ranked. Then the reliability sensitivity with respect to the distribution parameters of redundant components is studied. The results of the reliability sensitivity analysis investigate the effects of the distribution parameters on the entire system's reliability. The effectiveness and feasibility of the proposed methodology are demonstrated by the successful application on the hydraulic system of wind turbines. • Load-sharing based reliability model using survival signature is proposed. • Reliability-redundancy allocation optimization is conducted using the GA. • Reliability importance analysis and reliability sensitivity analysis are performed. • Reliability of redundant components can be improved considering load-sharing. [ABSTRACT FROM AUTHOR]

The submarine pipeline stopper is an emergency device that can quickly seal damaged pipelines. Investigating the stopper's dependability and safety is vital to ensure that subsequent maintenance activities go smoothly. However, in production environments, it is challenging to get failure data for complex systems due to high experimental costs. This work proposes a fuzzy comprehensive dynamic Bayesian network (FCDBN) based on fault tree, fuzzy evaluation, and dynamic Bayesian network. Using this method, the failure rate of the stopper can be obtained, thus solving the problem of difficult data acquisition. The time slice-based qualities are taken into account while evaluating the reliability and safety. By controlling variables, each failure rate's effect is quantified. Finally, the process of failure prediction is completed. The sealing device is least reliable and most possible to fail, according to the results. The most significant influence on reliability comes from rubber barrel shoulder upwarping. The failure rate of the stopper is highest if the sealing device fails. Based on the aforementioned findings, appropriate control measures are suggested, which can greatly lower the stopper failure risk. • Considering the possibility of degradation, a fuzzy comprehensive dynamic Bayesian network (FCDBN) is proposed and it is applied to the submarine pipeline stopper. • The main failure modes of the stopper were analyzed and the failure rates were obtained, thus filling the gap in the lack of data for the stopper. • The reliability, safety, and impact of failure modes on thestopper were analyzed. FCDBN also can be used for reverse analysis to predict the failure probability of each mode. • The results indicate that the safety of the stopper reduces over time, and the sealing device has the lowest reliability and is most prone to failure. Thus, when designing and producing, more attention should be paid to the sealing device. [ABSTRACT FROM AUTHOR]

• An enhanced FE analysis procedure is adopted for performing the deterministic and stochastic analyses. • The main components in high-rise buildings, including column, rectangular, and U-shaped shear walls are considered. • A simplified strategy is developed to represent the random field for the U-shaped wall. • An efficient framework is developed to capture the stochastic response and assess the structural reliability. • The failure probability is determined through the developed framework. The unavoidable heterogeneity in the mechanical characteristics of concrete is widely acknowledged. Although it is widely considered as either perfectly correlated or entirely independent random variables in engineering practice; however, such treatment is illogical, and the outcomes may be deceptive. In high-rise buildings comprised of multiple structural components, it is crucial to consider the material properties' spatial variability (MPSV) to obtain a reliable structural response and avoid damage to these structures. To this end, three main components, including column, rectangular shear wall, and U-shaped shear wall, are considered herein to investigate their stochastic response. The MPSV is represented by a covariance matrix decomposition-based random field generator combined with a GF-discrepancy-based point selection strategy to generate samples efficiently. A simplified strategy is developed to represent the random field for the U-shaped wall. Moreover, the probability density evolution method combined with the extreme value event is employed to obtain the failure probability of the studied components, where failure probabilities of 18%, 23%, and 32% are recorded for the studied RC column, rectangular shear wall, and U-shaped shear wall, respectively. Furthermore, different failure modes were identified and could not be determined through the deterministic analysis, highlighting the importance of accounting for material uncertainty. The proposed framework proved that the stochastic response and non-linear behavior of the considered components could be well captured and provide full perspective about the uncertainty quantification and reliability assessment and can be further implemented to capture the stochastic response and safety assessment of high-rise buildings. [ABSTRACT FROM AUTHOR]

The changing climate may influence building performance in several different aspects. In respect to the hygrothermal performance, excessive moisture arising from climate loads and present in wall assemblies over a prolonged period of time is the cause of many detrimental effects on wall components; notably, such effects may become more significant under a changing climate. The occurrence of these effects can be assessed by undertaking hygrothermal simulations. However, numerical simulations can be time-consuming, especially when evaluating the detrimental effects over a long period, which is necessary for assessing the impacts of climate change on building envelope components. A means to reduce such a time consuming and costly simulation effort is to select a set of representative years from the series of long-term climate data; a set of Moisture Reference Year (MRY), with the expectation of obtaining similar results as those obtained from the long-term simulations. This study assesses the reliability of using MRY for evaluating the long-term hygrothermal performance of wall assemblies. The MRY were selected based on moisture index (MI) rankings for historical and future climatological periods. The simulations using MRY were repeated at least 2 times and up to 10 times, with the results compared to those obtained from 31 years of consecutive simulations. Two types of wood-frame wall assemblies, each with different types of exterior cladding, were analyzed. Several criteria were selected for comparison. The results showed that the required number of repetitions of MRY is determined by the parameters used for comparisons. • Assessed the reliability of using moisture reference years (MRYs). • Determined the required number of repetitions of MRYs under different criteria. • Moisture index was used as the criteria for the MRYs selection. • The MRYs approach is suitable for assessing changes in wood frame wall performance. [ABSTRACT FROM AUTHOR]

Reliability analyses based on probability theory are widely applied in geotechnical engineering, and several analytical or numerical methods have been built upon the concept of failure occurrence. Nevertheless, common geotechnical engineering real-world problems deal with scarce or sparse information where experimental data are not always available to a sufficient extent and quality to infer a reliable probability distribution function. This paper rigorously combines Fuzzy Clustering and Possibility Theory for deriving a data-driven, quantitative, reliability approach, in addition to fully probability-oriented assessments, when useful but heterogeneous sources of information are available. The proposed non-probabilistic approach is mathematically consistent with the failure probability, when ideal random data are considered. Additionally, it provides a robust tool to account for epistemic uncertainties when data are uncertain, scarce, and sparse. The Average Cumulative Function transformation is used to obtain possibility distributions inferred from the fuzzy clustering of an indirect database. Target Reliability Index Values, consistent with the prescribed values provided by Eurocode 0, are established. Moreover, a Degree of Understanding tier system based on the practitioner's local experience is also proposed. The proposed methodology is detailed and discussed for two numerical examples using national-scale databases, highlighting the potential benefits compared to traditional probabilistic approaches. • Novel non-probabilistic and data-driven reliability method. • Average Cumulative Function used as Probability to Possibility transformation. • Possibilistic Reliability Targets consistent with the probabilistic values provided by the Eurocode 0. • Three-tier Degree of Understanding system for considering Local Experience and Subjective Information. • Numerical applications on shallow and pile group foundations performed by using indirect databases. [ABSTRACT FROM AUTHOR]

Large-scale renewable integration presents an effective way to decarbonize power grids, but carries increased risk of supply shortfalls owing to its volatility and uncertainty. Storage is a promising option to improve the generation adequacy of renewable. Thus, capacity credit assessment of renewable and storage is crucial in ensuring adequate generation capacity to meet loads. However, efficiently and accurately assessing capacity credit of these resources is challenging due to temporal dependencies in the operational time series (net loads and conventional generation units) and the need for extensive operation simulations. This paper develops a comprehensive multi-time-scale assessment framework integrating analytical and simulation methods to calculate the capacity credit of renewable and storage, thus capturing temporal features of these time series. Then, an interval-based strategy is proposed to simulate system operations, incorporating demand response in key scenarios. Furthermore, partitioning around medoids clustering and parallel computing techniques are employed to greatly accelerate the numerous operations for capacity credit assessment. The proposed method is validated using the RTS-79 system and a provincial real-world power grid in China. The results indicate that the developed framework can achieve efficient and refined capacity credit assessment and thus evaluate the impact of storage on the capacity credit. • Propose a novel method to quantify temporal features of operational time series for capacity credit (CC) assessment. • Develop a multi-time-scale CC assessment framework with an interval-based strategy to capture temporal features. • Utilize partitioning around medoids clustering and parallel computing to greatly enhance calculation efficiency. [ABSTRACT FROM AUTHOR]

• Inconsistency within the data of random variables is identified. • An improved formulation of probability modeling of random variables is proposed. • The realization of the proposed approach in the practical numerical analysis is presented. The aim of the present paper was to formulate probabilistic modeling for random variables with inconsistent data to facilitate accurate reliability assessment. Traditionally, random variables have some outputs available, based on which, some distribution is identified. However, as will be illustrated, the data relevant to those extreme events might not necessarily follow the same distribution as well as the other part, but they generally have small weights in the definition of the distribution due to their small quantity. The adoption of one single probabilistic distribution to describe random variables with such inconsistent data might cause great errors in the reliability assessment, especially for extreme events. One new formulation of probabilistic modeling is proposed here for such type of random variables. The inconsistency within the data set is identified and based on how the set is divided. Each division is described by the respective distribution and finally they are unified into one framework. The relevant problems in the modeling (e.g., the identification of the boundary between the divisions, the definition of the probability distributions, and the unification of the distributions into one framework) are presented and solved. The realization of the proposed approach in the practical numerical analysis is further investigated afterwards. Finally, two examples are presented to illustrate the application from different perspectives. [ABSTRACT FROM AUTHOR]

Highlights: • Cover wind speeds and multi-state units in power system reliability assessment. • Infuse correlation structure into cross entropy based importance sampling method. • Positive correlation of wind speeds has adverse impacts on power system reliability. • Positive correlation of wind speed and load improves the power system reliability. Abstract With the rapid integration of wind energy, the increasing uncertainty and high reliable property of power systems have resulted in great difficulties in reliability assessment. To solve the problem, traditional cross entropy based importance sampling (CE-IS) methods are improved in this paper. The improved method is capable of efficiently assessing the reliability of composite power systems with wind energy integrated. First, we introduce the differences between the improved CE-IS (ICE-IS) and traditional CE-IS. Particularly, ICE-IS takes the correlation of random variables (RVs) into account and models multi-state RVs with multinomial distribution. Therefore, ICE-IS can obtain much better suboptimal distributions for the RVs than CE-IS, which accelerates the reliability assessment. Then the procedures of ICE-IS are detailed by two parts, which are a pre-simulation stage and a main simulation stage, respectively. Finally, we modify IEEE-RTS 79 and IEEE-RTS 96 test systems based on wind speed data observed in northwest China. Several case studies are designed and carried out on the modified systems to validate the proposed method. [ABSTRACT FROM AUTHOR]

• Solving the Reliability Index Approach (RIA) by evolutionary algorithms (EAs). • A hybrid genetic algorithm searches the global most probable failure point (MPP). • New evolutionary operators: genetic repair and reduction/reallocation of the search domain. • Implicit analysis of laminated composite structures with multivariate uncertainty space. • Comparison with gradient-based algorithm and Monte Carlo simulation. With uncertainty, reliability assessment is fundamental in structural optimization, because optimization itself is often against safety. To avoid Monte Carlo methods, the Reliability Index Approach (RIA) approximates the structural failure probability and is formulated as a minimization problem, usually solved with fast gradient-methods, at the expense of local convergence, or even divergence, particularly for highly dimensional problems and implicit physical models. In this paper, a new procedure for global convergence of the RIA, with practical efficiency, is presented. Two novel evolutionary operators and a mixed real-binary genotype, suitable to hybridize any Evolutionary Algorithm with elitist strategy, are developed. As an example, a shell laminate structure is presented and the results validated, showing good convergence and efficiency. The proposed method is expected to set the basis for further developments on the design optimization of more complex structures with multiple failure criteria. [ABSTRACT FROM AUTHOR]

The application of hysteretic damper has gained major attention in seismic resistant design of buildings. It provides an efficient and cost-effective solution to reduce the level of damage induced on the building due to seismic excitations. The efficiency of the damper is influenced by parameters such as yield strength, yield displacement and brace-damper assembly system stiffness. The premise of this paper is to investigate the effect of the uncertainty associated with the hysteretic damper design parameters on the probabilistic seismic performance of steel buildings. Two steel buildings which are designed in accordance with the European Standard design code are evaluated. The uncertainties associated with the damper design parameters are incorporated using the Optimised Latin Hypercube sampling method for different confidence levels. The building response is obtained by conducting nonlinear time history analyses in OpenSEES. The annual frequency of exceeding a damage limit state, which is quantified by integrating the seismic fragility curves and hazard curves, is computed for the steel buildings with and without consideration of the design parameter uncertainties. • This paper provides a detail seismic performance assessment of steel building with hysteretic steel damper. • The effect of uncertainty in damper parameters on seismic demand on the building was quantified using probabilistic approach. • Seismic fragility curves were developed for steel buildings with and without dampers and variability in drift hazard curves were derived. • Uncertainty of damper properties affect the annual probability of exceedance significantly for immediate occupancy limit state up to 24%. • The effect of damper parameter uncertainty is less conclusive for variability up to 10%. [ABSTRACT FROM AUTHOR]