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Probabilistic reliability assessment is an important part of life management for critical equipment, but it can be costly due to the need for extensive data. To further implement reliability assessment in engineering, it is essential to reduce both economic costs and the learning curve for engineers. This paper presents a surrogate model-based probabilistic reliability assessment plug-in for ABAQUS that does not depend on any third-party software. The plug-in can automatically perform stochastic finite element method considering multiple uncertainty sources including material, geometry, and load. By using the data obtained from FEM, the plug-in trains the surrogate model and completes the reliability assessment and visualization. This paper illustrates the theoretical basis, design concepts, and functionalities of the plug-in, along with an example demonstrating its effectiveness and efficiency. The free plug-in serves as a valuable tool for engineers, facilitating easy and efficient reliability assessments.

Reliability studies serve as valuable tools for assessing and optimizing system performance. Utilities with higher reliability indices are more likely to achieve break-even points due to significantly reduced downtime. This paper explores a comparative assessment of two 11 kV feeders supplying electricity to residential and commercial customers, addressing concerns about distribution system reliability in Nigeria and its impact on the country's GDP. The study involves a comprehensive reliability analysis, utilizing a flowchart to outline procedural steps and employing the ETAP Software program for data analysis collected over a one-month period from a power utility company. The data encompass operational parameters such as day-hourly consumption, outage records, and network equipment data. Results indicate higher reliability indices in the commercial feeder compared to the residential feeder, with the Customer Average Interruption Duration Index (CAIDI) being lower in the commercial feeder. The research underscores the significance of reliability assessment in improving operational efficiency, facilitating maintenance planning, and enhancing customer satisfaction.

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.

This work presents a new approach for performing a parametric study and examining nonlinear material behaviours of a coupled thermal-mechanical model of a Power Electronics Module (PEM) by integrating the Finite Element Method (ANSYS-FEM) with Parametric Model Order Reduction (pMOR). The considered coupling method solves the thermal and structural models concurrently compared to the widely practised sequential coupling method. Instead of constant parameter values, which are generally regarded for pMOR studies, the temperature-dependent material properties of the wire material have been parametrised in the work using the pMOR method. A generalised 2D model has been regarded here for thermal-mechanical analysis with the pMOR approach, parametrising temperature-dependent coefficient of thermal expansion (CTE) and Young’s modulus (E) of the wire material to explore their impact on wire bonds. The matrix interpolation method has been applied here for the pMOR study, and PRIMA, a Krylov subspace-based model order reduction (MOR) technique, has been exercised for local model order reductions. A new efficient process of matrix interpolation, based on the Lagrange interpolation technique, has been developed to implement matrix interpolation in the parametric reduced order model (pROM). The local reduced order models (ROMs) have a degree of freedom (DOF) of just 8, compared to the full-order models’ (FOMs) of 50,602. The pROM provides an excellent solution and reduces computational time by 84% for the presented case.

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.

City gas stations (CGSs) play a crucial role in ensuring a stable and safe supply of natural gas to urban users. However, as the service time of stations increases and the performance of components deteriorates, concerns about the safety and reliability of these station have grown among operators and local government authorities. This paper proposes a fuzzy reliability assessment methodology for CGSs that considers the polymorphism of component faults and the uncertainties associated with fault relationships, failure probabilities, and fault magnitudes. The methodology utilizes T-S fuzzy gates to describe the correlation among events and constructs a T-S fuzzy fault tree for CGSs. Component fault states are represented using fuzzy numbers, and a fuzzy group decision-making approach is introduced to evaluate the current fault magnitude of components. To handle the uncertainty caused by sparse failure sample data, a Bayesian updating estimation method is presented to estimate the failure probabilities of components. Furthermore, T-S fuzzy importance analysis is applied to identify the weak points in the CGS system. The effectiveness of the developed methodology is demonstrated through a case study of reliability analysis of a city gas distribution station. The research findings provide valuable support for optimizing the design and implementing preventive maintenance of CGSs.

With the increasing demand for highly efficient lighting in the automotive industry, flip-chip light-emitting diodes (LEDs) have become widely used for both interior and exterior lighting. Solder, serving as a crucial interconnecting material, often develops voids during the reflow process, compromising the integrity and reliability of the connections. Thus, understanding the impact of these voids on the mechanical and thermal properties of the product is vital for improving reliability accuracy. This work employs computational methods alongside experimental approaches to address the challenges of replicating solder voids and controlling the solder void fraction. A comprehensive study investigates the effects of solder voids on shearing properties and thermal conductance. Random voids were introduced into the solder pads of an LED assembly within a finite element model (FEM), leading to predictions of maximum shear stress and LED junction temperature. The findings correlate well with the experimental data, validating the FEM's applicability. Furthermore, a statistical analysis was conducted to explore the relationship between solder void fraction, position, and size, aiming to provide objective guidelines for analyzing soldered assembly tomography in reliability assessments.

Due to the temporal coupling constraints of energy storage, the reliability of modern renewable power systems can no longer be assessed by non-sequential methods. However, the existing sequential methods cannot be combined with variance reduction methods as easily as the non-sequential ones. Hence, the power system reliability assessment is faced with incredible computational burden. In this paper, we propose an improved sequential importance sampling method to accelerate the computation of power system reliability indices. The proposed method is composed of two stages, namely the pre-simulation and main simulation stages. In the pre-simulation stage, we design a customized probability distribution parameter optimization process for load curves in the cross entropy based importance sampling method. In the main simulation stage, the derived probability distribution parameters in the pre-simulation stage are converted and further used to carry out a sequential Monte Carlo sampling process to efficiently estimate the reliability indices of renewable power systems with energy storage. The proposed method is validated by numerical tests performed on the modified IEEE-RTS 79 and IEEE-RTS 96 test systems, both of which are integrated with three wind farms, one photovoltaic station and one battery energy storage power station. Results show that the proposed method is much faster than existing sequential importance sampling methods in both test systems because of exploiting the acceleration potential of the load. Therefore, the proposed method can be used to assess the reliability of the renewable power systems with more energy storage devices in the future.

With the rise in carbon emissions, it has become fashionable to construct a new power system using new energy as the primary raw material for power generation. This, however, might result in a significant rise in the number of system states and frequent changes in the system state. These issues raise the bar for the effectiveness of system dependability evaluation. Traditional evaluation procedures necessitate computing Optimal Power Flow (OPF) for many systems states one by one, which is unquestionably time-consuming. For that purpose, this research proposes a reliability evaluation approach based on two-dimensional convolutional networks. Branch circuit fault messages are analyzed with generator fault messages. Multiple channel state data from power system node admittance matrices, power generation, and energy demand are fed into a two-dimensional convolutional neural network. This is used to determine the regression connection between load curtailments and system status data and to forecast load curtailments. Finally, it is applied to the RTS-79 system, and experimental results demonstrate that the technique has certain advantages in terms of computing speed and accuracy.

During a routine hole probe inspection, the aviation division found that the combustion chamber flame tube of a V2500 aero engine had failed early and that the TBCs around the vent holes on the flame tube liner panel 4 had peeled off over a large area, causing the flame tube liner to burn through. This work studies the premature failure of the V2500 aero-engine flame tube and, using modeling and simulation tests, finds the reason for premature failure as a substantial area of thermal barrier coating peeling owing to sintering. The sintering procedure significantly increases the thermal barrier coating's Young's modulus, which reduces the coating's resistance to thermal mismatch strain. The reliability of the TBCs was then evaluated using a probabilistic approach. Owing to the non-uniformity of temperature distribution induced by the hot airflow, the lifetime of the TBCs in the high temperature region is only around 3000 h, but the lifetime in the low temperature region is at least 25,000 h. Using the method of hole-probing, the values of the calculated and real outcomes are compared. The relative error is less than 5 %, demonstrating that the suggested approach for evaluating dependability has high credibility and applicability.

The FIDES Guide is a reliability prediction handbook published by a group of European defense and aerospace manufacturers under the supervision of the French Ministry of Defense. This paper evaluates the suitability of FIDES for predicting reliability. The evaluation is based on the fitness of the model used in FIDES for calculating the failure rate and the accuracy of its reliability predictions. The assessment makes use of the questionnaire in the IEEE Standard 1413, “IEEE Standard Framework for Reliability Prediction of Hardware.” The foundation of FIDES on the erroneous assumption of a constant failure rate for electronics, its omission of critical attributes (e.g., materials and geometry of components), and the subjectivity of certain inputs to the model make it inadequate for producing an accurate prediction.

Under the global zero-carbon campaign (United Nations Climate Change, 2021), more stochastic renewable generations are being integrated into the system. This trend significantly expands the state space and increases the computational burden for the model-driven reliability assessment. Data-driven approaches are developed to improve efficiency based on artificial intelligence. However, the requirement of large-scale samples limits it in applications. To address that, this paper adopts a self-supervised stage to avoid the high cost of labeling, while ensuring the efficiency and accuracy of reliability assessment. The training process of this method is split into two stages. In the first stage, feature reconstruction and unsupervised learning are used to provide the initial network parameters. Thereafter, the second learning stage can be trained in a task-agnostic way with fewer labels. The results of case study demonstrate the effectiveness of the proposed approach.

This paper presents a new efficient reliability assessment method for multiple energy systems (MES) based on shadow price (SP). The coupling and interaction of multiple energy flows are quantitatively described by the energy hub (EB) model. The shadow price theory can accelerate the calculations of optimal load shedding in the reliability assessment for MES. Large numbers of system states are generated by the fluctuations of renewable energy generation. These numerous system states can be evaluated by the shadow price theory instead of cumbersome optimization computations. The case study shows that the proposed SP approach can greatly improve the efficiency of MES.

Renewable-energy-based generating systems like photovoltaic (PV) systems, and energy storage devices like batteries are mostly considered as two types of main generation sources for the islanded operation mode of a low-voltage DC microgrid (LV-DC MG). Dynamic characteristics of the off-grid LV-DC MG, for instance, fluctuation in load power or frequent changes in the PV-system output power; as well as transient characteristics such as DC faults, and switching actions of power devices may reduce the converters’ reliability. To explicate the viewpoints mentioned, this paper will evaluate the reliability of DC-DC step-up converters used for renewable energy-based generation systems, typically for the PV systems of the LV-DC MG from different dynamic and fault conditions. To perform the reliability analysis, the dynamic-voltage-dependent (DVD) failure rate (FR) and fault-current-dependent (FCD) failure rate of the PV-system step-up converter will be formularized. More clearly, the assessment of step-up converter reliability and its discrete components is based on a new combination of the used-time-dependent (UTD) failure rate, FCD-FR, and DVD-FR and the use of a Markov-state transition diagram. Experimental results illustrate that the output capacitor of the boost converter is greatly influenced by the transient and dynamic characteristics of the PV-generating system. The DVD-FR of the boost converter is quite small when compared to its FCD-FR. Moreover, the PV-system boost converter’s reliability is most rapidly decreased by multiple repeated dynamic cases when the LV-DC MG is in the islanded mode. In addition, when considering both dynamic and transient characteristics of the LV-DC MG, two indices, namely, mean-time-to-failure (MTTF), and mean-time-between-failures (MTBF) of the PV-system boost converter could also be quickly reduced.

This paper introduces a novel technique for optimal distribution system (DS) planning with distributed generation (DG) systems. It is being done to see how active and reactive power injections affect the system’s voltage profile and energy losses. DG penetration in the power systems is one approach that has several advantages such as peak savings, loss lessening, voltage profile amelioration. It also intends to increase system reliability, stability, and security. The main goal of optimal distributed generation (ODG) is a guarantee to achieve the benefits mentioned previously to increase the overall system efficiency. For extremely vast and complicated systems, analytical approaches are not suitable and insufficient. Therefore, several meta-heuristic techniques are favored to obtain better performance from were convergence and accuracy for large systems. In this paper, an Improved Wild Horse Optimization algorithm (IWHO) is proposed as a novel metaheuristic method for solving optimization issues in electrical power systems. IWHO is devised with inspirations from the social life behavior of wild horses. The suggested method is based on the horse’s decency. To assess the efficacy of the IWHO, it is implemented on the 23 benchmark functions Reliability amelioration is the most things superb as a result of DGs incorporation. Thus, in this research, a customer-side reliability appraisal in the DS that having a DG unit was carried out by a Monte Carlo Simulation (MCS) approach to construct an artificial history for each ingredient across simulation duration. For load flow calculations, the backward Forward Sweep (BFS) technique has been employed as a simulation tool to assess the network performance considering the power handling restrictions. The proposed IWHO method has been measured on IEEE 33 69 and 119 buses to ascertain the network performing in the presence of the optimal DG and the potential benefits of the suggested technique for enhancing the tools used by operators and planners to maintain the system reliability and efficiency. The results proved that IWHO is an optimization method with lofty performance regarding the exploration–exploitation balance and convergence speed, as it successfully handles complicated problems.

The damage of lightweight aggregate high performance concrete (LHPC) under four-point bending was experimentally investigated. The effects of stress level and loading age on LHPC damage were elucidated. The research results show that with the increase of stress level, the damage of LHPC is gradually aggravated, but it also stimulates the internal curing effect of lightweight aggregate (LWA). The internal curing effect of pre-wetted lightweight aggregate (PLWA) promotes the formation of hydration products such as C-S-H gel, which can slow down the damage of LHPC under loading. When the stress level is 50%− 70% ultimate load (Pcr), LHPC is partially damaged under continuous bending, and its corrosion resistance coefficient (Kf) and relative dynamic elastic modulus (Erd) are reduced, but it still has a certain carrying capacity. The LHPC under the action of 90% Pcr was completely damaged in 11588 s, and the LHPC under the action of 95% Pcr was completely damaged in 1096 s. Under a given stress level, the fatigue life of LHPC can be simulated by a two-parameter Weibull distribution. Therefore, a reliability evaluation method is proposed to obtain the fatigue life prediction of LHPC under 4-point bending loading.

The optimal load shedding calculations of an enormous number of contingency states are the most challenging impediments for power system reliability assessment, especially in a large-scale system with multiple types of loads. To deal with that, this paper proposes a fast method that accelerates the calculation of optimal load shedding based on the shadow price. This method calculates the optimal load shedding of systems by shadow-price-based linear functions, rather than the time-consuming OPF algorithms. The hundred percentage criterion is used to look for the matching shadow price of states. Case studies are performed on the RTS-79 system. The results show that the proposed methodology can significantly simplify the solving process and reduce the calculation time.

Embryonic developmental effects of disinfection by-products, which are generated during drinking water treatment and widely detected in environment, have gained more and more attention nowadays, calling for construction of in vitro research models which can mimic early embryonic development to evaluate the embryotoxicity. The embryonic stem cell test offers a promising assay to predict embryotoxicity of environmental pollutions. However, it is not appropriate for the toxicological study of preimplantation embryos. Here, we used mouse extended stem cells (mEPS) to reconstruct embryo-like structures (blastoid), furtherly attempting to evaluate the reliability of this model for the prediction of possible developmental toxicity of 2,4,6-triiodophenol (TIP, 5–50 μM), a novel halogenated disinfection byproduct widely detected in water and even drinking water, to mammalian preimplantation embryo. To verify this, we treated mouse embryo derived from in vitro fertilization (IVF-embryo) as reference. The results showed that mEPS-blastoid was like natural blastocyst in morphology, cell composition, and could recapitulate key developmental events happened during mouse preimplantation stage. When blastoid and IVF-embryo models were separately exposed to TIP, their final blastocyst formation rates were not impaired, according to morphological features, meanwhile that TIP exposure caused slight cell apoptosis. Besides, TIP induced an ICM cell bias in cell fate decision, resulting in cell proportion change, which implied abnormal developmental potential. Though we could not evaluate TIP’s embryotoxicity before 8-cell stage using blastoid model, its viability as a novel and high-throughput assessment platform for increasing environmental pollutants was still recognized.

One of the major concerns of the utility companies is to ensure that the generation capacity (GC) is maintained above the load growth. The demand for assessment of the GC at non-distant time intervals is thus crucial. In general, the rising load is linearly proportional to the generation deficiency. A linear regression approach had been successfully developed to predictively accommodate this demand to avert the issue of deficit supply arising from an unforeseen delay. The Monte Carlo (MC) technique was used in the generation system (GS) modeling. However, due to the inherent stochasticity associated with the MC algorithm, the emerging graphical relationship between the load and the generation deficiency is generally linear but always maintains nonlinearity at various intervals along the gradient curve. This paper integrates the artificial neural network (ANN) nonlinear feature using the Levenberg–Marquardt training algorithm with the MC simulation to accommodate the MC-associated nonlinearities to improve the generation system reliability (GSR) prediction. The generalization performance of the prediction obtained on the test data was found to have been greatly improved.

Embryonic developmental effects of disinfection by-products, which are generated during drinking water treatment and widely detected in environment, have gained more and more attention nowadays, calling for construction of in vitro research models which can mimic early embryonic development to evaluate the embryotoxicity. The embryonic stem cell test offers a promising assay to predict embryotoxicity of environmental pollutions. However, it is not appropriate for the toxicological study of preimplantation embryos. Here, we used mouse extended stem cells (mEPS) to reconstruct embryo-like structures (blastoid), furtherly attempting to evaluate the reliability of this model for the prediction of possible developmental toxicity of 2,4,6-triiodophenol (TIP, 5-50 μM), a novel halogenated disinfection byproduct widely detected in water and even drinking water, to mammalian preimplantation embryo. To verify this, we treated mouse embryo derived from in vitro fertilization (IVF-embryo) as reference. The results showed that mEPS-blastoid was like natural blastocyst in morphology, cell composition, and could recapitulate key developmental events happened during mouse preimplantation stage. When blastoid and IVF-embryo models were separately exposed to TIP, their final blastocyst formation rates were not impaired, according to morphological features, meanwhile that TIP exposure caused slight cell apoptosis. Besides, TIP induced an ICM cell bias in cell fate decision, resulting in cell proportion change, which implied abnormal developmental potential. Though we could not evaluate TIP's embryotoxicity before 8-cell stage using blastoid model, its viability as a novel and high-throughput assessment platform for increasing environmental pollutants was still recognized., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 ISA. Published by Elsevier Ltd. All rights reserved.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 ISA. Published by Elsevier Ltd. All rights reserved.)