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ABSTRACT Adhesively bonded joints made of carbon fiber reinforced polymer (CFRP) are critical structures of offshore wind turbine blades, yet they are particularly susceptible to fatigue failure influenced by marine environmental factors. Current methodologies for predicting the fatigue life and assessing the reliability of these bonded joints face limitations due to insufficient data, modeling complexities, and inefficiencies. This study introduces a novel, physics‐informed, data‐driven approach to analyze the fatigue performance of CFRP adhesively bonded joints subjected to multi‐environmental stress, thereby improving fatigue life predictions and reliability assessments. Initially, an innovative method that integrates a physics‐informed data‐driven framework for predicting adhesive fatigue life and modeling reliability is proposed, utilizing the cyclic cohesive zone model (CCZM) alongside single‐hidden layer feedforward neural networks (SLFN). Multi‐environmental aging tests were conducted on CFRP‐bonded joints of varying dimensions, leading to the development of a physics‐informed fatigue analysis method based on CCZM. Furthermore, a reliability assessment and sensitivity analysis were performed to evaluate the impact of uncertainty factors. This research provides significant insights for the structural design and safety analysis of bonded structures in offshore wind turbine blades. [ABSTRACT FROM AUTHOR]

Shallow-water bathymetric maps provide vital geographic information for various coastal and marine applications such as environmental management, engineering construction, oil and gas resource exploration, and ocean fisheries. Recently, satellite-derived bathymetry (SDB) has emerged as an alternative approach to shallow-water bathymetry, particularly in hard-to-reach areas. In this research, an innovative approach to bathymetry was introduced. This method provides a reliable approach for generating high-accuracy and high-reliability shallow water bathymetry results. By using Sentinel-2 time series imagery combined with ICESat-2 data, four bathymetry results at different time points are produced based on four traditional bathymetry methods. For the results at each location, a statistical method is applied to evaluate the bathymetry results, remove erroneous data, and generate high-confidence bathymetry results. The validation results indicated that the accuracy of the proposed bathymetric method achieved an R² range of 0.96 to 0.99 and an RMSE between 0.42 and 1.18 meters. When contrasted with traditional methods that utilize a single temporal image, a notable enhancement in bathymetric accuracy was observed. [ABSTRACT FROM AUTHOR]

The assessment of rolling bearing reliability is vital for ensuring mechanical operational safety and minimizing maintenance costs. Due to the difficulty in obtaining data on the performance degradation and failure time of rolling bearings, traditional methods for reliability assessment are challenged. This paper introduces a novel hybrid method for the reliability assessment of rolling bearings, combining the convolutional neural network (CNN)‐convolutional block attention module (CBAM)‐ bidirectional long short‐term memory (BiLSTM) network with the Wiener process. The approach comprises three distinct stages: Initially, it involves acquiring two‐dimensional time‐frequency representations of bearings at various operational phases using Continuous Wavelet Transform. Subsequently, the CNN‐CBAM‐BiLSTM network is employed to establish health index (HI) for the bearings and to facilitate the extraction of deep features, serving as input for the Wiener process. The final stage applies the Wiener process to evaluate the bearings' reliability, characterizing the HI and quantifying uncertainties. The experiment is performed on bearing degradation data and the results indicate the effectiveness and superiority of the proposed hybrid method. [ABSTRACT FROM AUTHOR]

Copyright of Umm Al-Qura University Journal of Engineering & Architecture (Springer Nature) is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a novel decision-making framework for the life cycle management of aircraft components, integrating advanced data analytics, artificial intelligence, and predictive maintenance strategies. The proposed model addresses the challenges of balancing safety, reliability, and cost-effectiveness in aircraft maintenance. By using real-time health monitoring systems, failure probability models, and economic analysis, the framework enables more informed and dynamic maintenance strategies. The model incorporates a comprehensive approach that combines reliability assessment, economic analysis, and continuous re-evaluation to optimize maintenance, replacement, and life extension decisions. The optimization method on the base of genetic algorithm (GA) is employed to minimize total life cycle costs while maintaining component reliability within acceptable thresholds. The framework's effectiveness is demonstrated through case studies on three distinct aircraft components: mechanical, avionics, and engine. These studies showcase the model's versatility in handling different failure patterns and maintenance requirements. This study introduces a data-driven decision-making framework for optimizing the life cycle management of aircraft components, focusing on reliability, cost-effectiveness, and safety. To achieve optimal maintenance scheduling and resource allocation, a GA is employed, allowing for an effective exploration of complex solution spaces and enabling dynamic decision-making based on real-time data inputs. The GA-based optimization approach minimizes total life cycle costs while maintaining component reliability, with the framework's effectiveness demonstrated through case studies on key aircraft components. Key findings from the case study demonstrate significant cost reductions through optimization, with mechanical components showing a 10% more reduction in total life cycle costs, avionics components achieving a 14% more cost reduction, and engine components demonstrating a 7% more decrease in total costs. The research also presents an optimized dynamic maintenance schedule that adapts to real-time component health data, extending component lifespans and reducing unexpected failures. The framework effectively addresses key industry challenges such as no fault found events while minimizing unexpected failures and enhancing the overall reliability and safety of aircraft maintenance practices. Sensitivity analysis further demonstrates the model's robustness, showing stable performance under varying failure rates, maintenance costs, and degradation rates. The study contributes a scalable approach to predictive maintenance, balancing safety, cost, and resource allocation in dynamic operational environments. [ABSTRACT FROM AUTHOR]

Introduction: The 1.2 °C rise of global ambient temperature since the pre-industrial era has led to an increase the intensity and frequency of heatwaves. Given the heightened vulnerability of pregnant women to heat stress, there is an urgent need for tools which accurately assess the knowledge, risk, and perception of pregnant woman toward heatwaves, enabling effective policy actions. In this research, we developed and validated tools to evaluate pregnant women's perceptions of heat wave risks and behaviors. Method: We developed 50 items across seven constructs using the Health Belief Model, identified through a systematic literature review. The constructs comprised 8 Knowledge(K) items, 4 in Perceived Vulnerability (PV), 5 in Perceived Severity (PS), 6 in Perceived Benefit (PB), 4 in Perceived Barrier (PBa), 5 in Cue to Action(Cu) and 18 in Adaptation(A). Cognitive testing was performed with a separate group of pregnant women(n = 20). The tested tools were then administered to 120 pregnant women residing during the spring-summer 2023. Construct validation utilized exploratory factor analysis. Results: The Principal Axis Factoring Method was employed in the EFA with oblimin rotation for 51 items, considering communality > 0.20, and aiming to extract three factors. Across the three factors with Cronbach's alpha > 0.70, a total of 11 items were distributed. Factor 1 included Perceived Severity (PS1, PS2, PS3 and PS5); Factor 2 included Cue to Action (Cu1, Cu2, Cu3, and Cu4); and Factor 3 encompassed Perceived Vulnerability (PV1, PV2, PV4). Only two of the retained items had factor loadings > 0.50, namely PV4 and PS5. Consequently, the three constructs measuring Perceived Severity, Cues to Action, and Perceived Vulnerability using the HBM among pregnant women were deemed valid. Conclusion: Our study has successfully validated a highly reliable tool which stands ready for application in assessing pregnant women's risk perception regarding heatwaves. [ABSTRACT FROM AUTHOR]

Structural resistance deterioration is by nature a non-increasing stochastic process with autocorrelation on the temporal scale. The Gamma process is often used to describe the stochastic behavior of resistance deterioration. With Gamma-based deterioration models, the calculation of time-dependent reliability presents a serious challenge, and often Monte Carlo simulation is the only solution to this problem. This paper derives a closed-form solution for time-dependent reliability of aging structures, in which the resistance deterioration is described by a Gamma process and the applied load is modelled as a Gaussian process with a constant standard deviation. The accuracy of the proposed method is verified through a comparison with Monte Carlo simulation results, and its applicability is further illustrated in a time-dependent reliability analysis of an existing highway bridge whose vehicle load is modelled using weigh-in-motion (WIM) data. It is found that the measured vehicle load has a relatively small uncertainty, and the uncertainty associated with resistance deterioration is crucial to the reliability assessment because it dominates the overall uncertainty in the reliability calculation. [ABSTRACT FROM AUTHOR]

With the rapid increase in the number of various terminal devices in distribution systems, the important impact of communication networks on power supply reliability in cyber–physical distribution systems (CPDSs) is becoming increasingly prominent. The traditional wired communication method makes it difficult to meet the demand in some scenarios due to high cost and construction difficulty. For this reason, this study evaluated the reliability of power distribution cyber–physical systems, considering fifth-generation (5G) mobile communication technology wireless access conditions. First, a reliability model of a communication channel in a 5G communication network was established, and the indirect impact of information system failure on the power distribution system was analyzed. Secondly, a reliability assessment method based on the improved least path method was devised by combining switch action failure, self-healing processing, and load transfer. The simulation results show that 5G communication technology has significant advantages in improving system reliability, and the proposed method is closer to the development direction of future power distribution systems, which provides an effective reference basis for the optimized operation of power distribution cyber–physical systems. [ABSTRACT FROM AUTHOR]

The preservation of the degrading transport infrastructure is vital, as replacing it is impossible in view of limited budgets and environmental impacts. One component in overcoming this challenge is the evaluation of the reliability and the calculation of the remaining service life of existing structures. These structural reliability reassessments allow for the identification and utilization of load‐bearing capacity reserves. The consideration of actual structural characteristics and environmental conditions, various of which can be gathered by means of numerous measurement and inspection techniques, has the potential for more realistic computation results and therefore more economic decisions about the operation as well as maintenance activities. This article attempts to shed light on the potential of nondestructive testing (NDT) methods for updating the input variables in the ultimate limit states during a recalculation. The approach of the NDT‐supported reliability reassessment is demonstrated using a prestressed concrete bridge emphasizing the bending proof in transversal direction. As a result, the semi‐probabilistic, probabilistic and NDT‐based reassessment results are compared and the effects of NDT‐supported structural analysis on the calculated reliability are highlighted. [ABSTRACT FROM AUTHOR]

Considering that the degradation of ball screw grease involves fractal characteristics, which exhibit long-term dependency and autocorrelation, a multivariate accelerated degradation modeling and reliability assessment method based on the fractional Brownian motion process model is proposed in this paper. Firstly, a nonlinear accelerated degradation model of grease is established using fractional Brownian motion, considering the heterogeneity of samples as well as the memory effect and long-term dependence in the deterioration process, and realizing parameter estimation. Secondly, a multivariate reliability evaluation model is established by considering multivariate performance indicators in combination with the Frank copula function. Finally, the effectiveness and potential engineering application value of this method are verified through actual degradation data of the grease. [ABSTRACT FROM AUTHOR]

Step‐stress accelerated degradation test (SSADT) has become a prevailing approach to lifetime assessment for highly reliable products. In practice, many products suffer from multiple degradation processes that significantly contribute to failures. In this paper, we investigate the optimal SSADT plans for products subject to two dependent degradation characteristics modeled by a bivariate inverse Gaussian process. The drift parameter of each process is assumed to be influenced by a common stress factor. A bivariate Birnbaum‐Saunders (BVBS)‐type distribution is employed to approximate the lifetime distribution and facilitate the derivation of the objective function. The optimal plans are prescribed under three common optimality criteria in the presence of constraints on test units and inspections. A revisited example of fatigue crack is then presented to demonstrate the proposed methods. Finally, the sensitivity of the SSADT plans is studied, and the results exhibit fair robustness of the optimal plans. [ABSTRACT FROM AUTHOR]

Establishing an accurate accelerated degradation model is paramount for ensuring precise reliability evaluation results. Unfortunately, current accelerated degradation tests often lack test groups for investigating multi‐stress coupled phenomena. Consequently, existing multi‐stress accelerated models fail to adequately consider the impact of stress coupling when data with stress coupling information is absent. This limitation leads to the development of inaccurate models, ultimately affecting the precision of reliability assessment. To address this challenge, this paper introduces a new modeling method for multi‐stress accelerated degradation models that takes into account stress coupling effects. The proposed modeling method aims to improve the accuracy of reliability assessment under multi‐stress conditions. In the proposed model, the main effect function of stress is determined based on existing single‐stress accelerated models. The coupling effect is first examined through the Multivariate Analysis of Variance (MANOVA), and then the functional form of the coupling effect function is determined from the given candidate functions through correlation analysis. Next, the coupling effect is incorporated into a Wiener process to establish a multi‐stress accelerated degradation model, and the two‐step estimation method combining Least Squares Method (LSM) and Differential Evolution Algorithm (DEA) is proposed. The accuracy and effectiveness of the coupling effect test method, model establishment, and parameter estimation method were validated using two Monte Carlo simulation experimental data sets. Finally, the superiority of the proposed model is demonstrated through examples, providing feasible ideas and technical support for the research on multi‐stress accelerated degradation modeling considering stress coupling. [ABSTRACT FROM AUTHOR]

Local buckling of steel plates is a major concern, as it dominates the plastic mechanism and reduces the ultimate strength of the steel plates depending upon their slenderness. At elevated temperatures, material nonlinearities result in a further reduction in the stability, stiffness, and strength of thin steel plates. Numerical simulations have been performed on stiffened and unstiffened steel plates at elevated temperatures under uniaxial compression load for different slenderness using commercially available FE software ABAQUS. The established finite element model, which depicts the local buckling behaviour and ultimate post-buckling strength of square and rectangular steel plates, has been the basis for a number of parametric investigations, all of which have been conducted using shell finite elements. The designed specification offered by North American Standards (ANSI/AISC 360-16) and European Standards (EN 1993-1-2), which employ a stress-based method to determine the ultimate post-buckling strength of steel plates for local buckling at elevated temperature, has also been compared with the obtained results. According to this investigation, EN 1993-1-2 produces results that are excessively conservative at higher temperatures. To overcome this, an attempt has been made by replacing the normal temperature parameter by a temperature-dependent non-dimensional slenderness ratio for predicting the ultimate strength of steel plates in fire exposure conditions. Further, this study provides some critical highlights on the behavior of steel plates of two different aspect ratios at elevated temperatures under compressive loads. The design formulations have also been proposed for estimating the ultimate strength of thin square and rectangular steel plates under compression. A good correlation has been observed in predicting the strength of the new design approach compared with existing experimental and computational results. The reliability and accuracy assessments of the proposed approach have been illustrated in brief. [ABSTRACT FROM AUTHOR]

To enhance the reliability of mine mechanical and electrical systems, promptly detect and prevent potential failures, improve mining production efficiency, ensure worker safety, and maintain normal equipment operation, a fault information data diagnostic system was established to collect fault data from mine mechanical and electrical systems. A Bayesian fault network was constructed and converted into a binary decision diagram using the ITE structure for qualitative and quantitative analysis of system faults. The results show that the constructed model achieved a diagnostic accuracy rate of over 98% across different mining software platforms. In an actual evaluation of a mine mechanical and electrical system, insulation aging or damage, overload or overheating, electronic component failures, and internal short circuits were found to have the highest impact on fault, approximately 0. 972. This significantly improved the accuracy and efficiency of fault diagnosis in mechanical and electrical systems and provided an effective assessment of underlying fault nodes. The findings can serve as a reference for fault diagnosis and reliability assessment in similar mechanical and electrical systems. [ABSTRACT FROM AUTHOR]

Due to the variability of train loads, soil properties, and other factors, the cumulative settlement of high-speed railway soft soil subgrade induced by cyclic trains holds strong randomness and evolutionary characteristics. Thus, the prediction of cumulative settlement and assessment of the subgrade status is important to ensure the safety of high-speed trains. For this purpose, a vehicle-track-subgrade interaction model is established for calculating the dynamic stresses of the subgrade, and then the cumulative settlement and its evolution characteristics are predicted. Furthermore, the direct probability integral method (DPIM) is introduced to implement the uncertainty quantification and reliability assessment of cumulative settlement, especially spatial distribution, 3D-probability density function and the variation of reliability index with load cycles. A comparison between Monte Carlo method and DPIM is performed to demonstrate the accuracy and efficiency of stochastic analysis with respect to cumulative settlement. The sensitivity and reliability assessment of the cumulative settlement under the cases of different speed, train load and soil strength are investigated by numerical examples to illustrate the robustness of the proposed method. The results show that cumulative settlement is most sensitive to soil strength, followed by train axle loads. Besides, a reliability assessment method proposed in this work, which considers cumulative settlement, provides guidance for the design and maintenance of subgrade in this paper. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Computational Mechanics / Jisuan Lixue Xuebao is the property of Chinese Journal of Computational Mechanics Editorial Office, Dalian University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The occurrence of a Loss Of Offsite Power (LOOP) event can be a major threat to nuclear safety due to the dependence of auxiliary systems on electrical energy. Probabilistic safety assessments of nuclear power plants require, thus, estimates of the frequencies and durations of such LOOP events. These estimates are usually based on past statistical data, which is not always relevant. Model-based approaches are thus needed. This paper proposes an analytical method to estimate the frequency and duration of switchyard-centered LOOP events, which constitute one of the four main categories of LOOP events. The proposed method is mainly based on the identification of active minimal cut sets, considering the behavior of circuit breakers against faults according to their coordination and selectivity. Adapted versions of the Risk Reduction Worth and Fussel–Vesely importance factors are proposed to evaluate the impact of components on the switchyard-centered LOOP event frequency. Furthermore, uncertainty analysis is developed and performed. Various generic plant connection schemes are used for application. Results demonstrate the applicability of the methodology to estimate the frequency and duration of switchyard-centered LOOP events, and to identify optimal ways to reduce the risk by modifying the switchyard configuration. [ABSTRACT FROM AUTHOR]

The Emergency safety barrier is one of the active technical barriers related to the safety of liquefied petroleum gas storage tanks. However, this study assesses the reliability of emergency safety barriers to help decision-makers understand how they can support decisions to reduce the risks associated with LPG storage. This paper aims to develop an integrated approach that uses an intuitionistic fuzzy sets aggregation procedure, subjective safety analysis, and emergency event tree analysis to handle uncertainty in the reliability assessment of emergency safety barriers. In addition, a case study on the reliability assessment of the emergency safety barriers of the LPG plant in Algeria based on the proposed methodology is provided and carried out to illustrate its effectiveness and feasibility. The results demonstrated the ability of intuitionistic fuzzy sets aggregation procedure and subjective safety analysis to provide highly reliable results and evaluate the reliability of emergency safety barriers. However, the classical event tree analysis does not consider the possibility of assessing the emergency consequences of different accident scenarios. Consequently, it only allows you to estimate the occurrence probability of accident scenarios. The results of this study show that the reliability of emergency safety barriers can be used to estimate the probability of emergency consequences under different accident scenarios, improve reliability, and help prioritize emergency improvement measures. The study provides scientific and operational references for analyzing the emergency consequences of various accident scenarios. [ABSTRACT FROM AUTHOR]

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.

Shallow-water bathymetric maps provide vital geographic information for various coastal and marine applications such as environmental management, engineering construction, oil and gas resource exploration, and ocean fisheries. Recently, satellite-derived bathymetry (SDB) has emerged as an alternative approach to shallow-water bathymetry, particularly in hard-to-reach areas. In this research, an innovative approach to bathymetry was introduced. This method provides a reliable approach for generating high-accuracy and high-reliability shallow water bathymetry results. By using Sentinel-2 time series imagery combined with ICESat-2 data, four bathymetry results at different time points are produced based on four traditional bathymetry methods. For the results at each location, a statistical method is applied to evaluate the bathymetry results, remove erroneous data, and generate high-confidence bathymetry results. The validation results indicated that the accuracy of the proposed bathymetric method achieved an R² range of 0.96 to 0.99 and an RMSE between 0.42 and 1.18 meters. When contrasted with traditional methods that utilize a single temporal image, a notable enhancement in bathymetric accuracy was observed.

The development of modern surveying methods, particularly, Terrestrial Laser Scanning (TLS), has found wide application in protecting and monitoring engineering and objects and sites of cultural heritage. For this reason, it is crucial that several factors a˛ecting the correctness of point cloud registration are considered, including the correctness of the distribution of control points (both signalised and natural), the quality of the process, and robustness analysis. The aim of this article is to evaluate the quality and correctness of TLS registration based on point clouds converted to raster form (in spherical mapping) and hand-crafted detectors. The expanded Structure-from-Motion (SfM) was used to detect the tie points for TLS registration and reliability assessment. The results demonstrated that affine detectors are useful in detecting a high number of key points (increased for point detectors by 8–12 times and for blob detectors by about 10–24 times), improving the quality and TLS registration completeness. For the registration accuracy of point cloud on signalised check points, the lower values can be noted for maximum RMSE errors for blob affine detectors than detectors and larger values for corner detectors and affine detectors (not more than 4 mm in the extreme cases, typically 2 mm). The commonly-applied target-based registration method yields similar results (di˛erences do not exceed – in extreme cases – 3.5 mm, typically less than 2 mm), proving that using affine detectors in the TLS registration process is and reasonable and can be recommended.

With the development of natural gas pipelines, the proportion of aged pipelines in service has been increasing, and corrosion remains a primary cause of pipeline failure. Regular inspections and reliability assessments are crucial to ensure the safe operation of pipelines. This study investigated an efficient reliability assessment method for corroded pipelines that considers in-line inspection intervals. First, this study compared the commonly used limit state equations for corrosion defects to select one suitable for X80-grade steel pipelines. Additionally, a Tail-Fit Monte Carlo Simulation (TF-MCS) algorithm was proposed to improve the computational speed by 30 times compared to traditional Monte Carlo simulations. Then, this study explored the inspection intervals used for reliability assessments of corroded pipelines. Finally, the parameter sensitivity was analyzed considering the yield strength, maximum operating pressure, and pipe diameter. This study ensures the reliable operation of corroded gas pipelines. [ABSTRACT FROM AUTHOR]

The occurrence of probabilistic common cause errors (PCCEs) in man‐machine systems can lead to multiple human errors being affected by common causes and will contribute greatly to the reliability of the system. In this paper, A reliability modeling method based on event tree (ET)‐fault tree (FT) model for man‐machine systems subjected to PCCEs is proposed. Under the influence of PCCEs, risk factors in the system are not independent, and human errors affected by the same common cause will occur with different probabilities. To describe the dependencies among risk factors, a PCCE gate is proposed to extend FTs in the ET‐FT model. To analyze the impact of common causes on the human error probabilities and quantify the extended FT, an explicit method and an implicit method based on the Cognitive Reliability and Error Analysis Method are proposed. The proposed quantification methods consider the different relationships among multiple common causes in a single model. Finally, the proposed methods are validated in the reliability assessment of the emergency response system under malfunction of the solar wing mechanism. [ABSTRACT FROM AUTHOR]

The two dependent competing risks are soft failure due to aging degradation and fragmentation caused by shocks and hard failure due to spring breakage caused by the same shock process. Considering the complexity of the product itself and the instability of the working environment, this study proposed a generalized reliability model for systems experiencing dependent competing failure processes (DCFPs) of degradation and random shocks, which considered two kinds of DCFP: (1) shock process could affect soft failure thresholds; (2) degradation process could affect hard failure thresholds. In case (1), we considered the effect that a cumulative number of shocks above a certain magnitude could have on the change in the soft failure threshold. In case (2), we considered not only the shock process's impact on the soft failure threshold but also the total degradation (including continuous degradation and sudden degradation caused by shock) on the hard failure threshold. The model captures the features that the shocks experienced by the system affect the degradation process, accelerating the system degradation and causing soft failures; the degradation process of the system affects the shock process, making the system more susceptible to failure from external shocks. Finally, an example using micro‐electro‐mechanical systems devices illustrates the effectiveness of the proposed approach with sensitivity analysis. [ABSTRACT FROM AUTHOR]

Trust and reputation systems play a pivotal role in modern decentralized environments, fostering cooperation and mitigating risks in various online interactions. This paper introduces a variational formulation approach to model and analyze trust and reputation systems. By formulating trust and reputation as variational problems, this approach offers a novel perspective on understanding the underlying mechanisms governing trust establishment. The variational formulation provides a mathematical framework to determine the equilibrium weighted trust values, taking into account that each trustee tries to maximize its gain, namely the benefit minus the costs. The paper illustrates the applicability of this variational formulation through a pletora of simulations, demonstrating its effectiveness in modeling trust and reputation systems. Insights gained from this approach offer valuable guidance for the design and implementation of more reliable and efficient trust and reputation mechanisms in decentralized environments [ABSTRACT FROM AUTHOR]

The Markov method is a common reliability assessment method. It is often used to describe the dynamic characteristics of a system, such as its repairability, fault sequence and multiple degradation states. However, the "curse of dimensionality", which refers to the exponential growth of the system state space with the increase in system complexity, presents a challenge to reliability assessments for complex systems based on the Markov method. In response to this challenge, a novel reliability assessment method for complex systems based on non-homogeneous Markov processes is proposed. This method entails the decomposition of a complex system into multilevel subsystems, each with a relatively small state space, in accordance with the system function. The homogeneous Markov model or the non-homogeneous Markov model is established for each subsystem/system from bottom to top. In order to utilize the outcomes of the lower-level subsystem models as inputs to the upper-level subsystem model, an algorithm is proposed for converting the unavailability curve of a subsystem into its corresponding 2 × 2 dynamic state transition probability matrix (STPM). The STPM is then employed as an input to the upper-level system's non-homogeneous Markov model. A case study is presented using the reliability assessment of the Reactor Protection System (RPS) based on the proposed method, which is then compared with the models based on the other two contrast methods. This comparison verifies the effectiveness and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

The reliability requirements for mechatronics continue to increase. Traditional life test methods make it difficult to obtain the data required to assess or predict the reliability of these mechatronic products within a reasonable time. Accelerated life tests can speed up the failure process of the product by using accelerated stress conditions. And then test data is obtained to predict the reliable life of the product under normal service conditions through modeling and analysis. The test efficiency of the accelerated life tests is higher than that of the life test. So, the method of accelerated life tests has become a solution for life and reliability prediction of highly reliable and long‐life mechatronic products. However, mechatronic products usually undergo cyclic stress during service. The use of cyclic stress as an accelerating stress in accelerated life tests can provide a realistic representation of service conditions and achieve higher test efficiency. Cyclic stress accelerated life test is an accurate and efficient solution for predicting the life and reliability of such mechatronic products. Current relative research is focused on constant stress, step stress and sequential stress accelerated life tests. A great challenge in engineering is modeling and analysis of cyclic stresses accelerated life tests. In this paper, we first describe the cyclic stress accelerated life test profile and its key parameters, then propose model assumptions and establish a modelling and analysis method for cyclic stress accelerated life test data, followed by an illustrative example to apply and validate the proposed method. The research results can provide technical support for the life and reliability assessment of mechatronic products under cyclic stress service conditions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The generalized iterative stochastic perturbation approach to the stress‐based Finite Element Method has been proposed in this work. This approach is completed using the complementary energy principle, Taylor expansion of the general order applicable to all random functions and parameters as well as nodal polynomial response bases determined with the use of the Least Squares Method. The main aim of this elaboration is the usage of such a probabilistic approach to determine Bhattacharyya relative entropy for some nonlinear engineering stress analysis with uncertainty. Mathematical apparatus with its numerical implementation has been used to study elastoplastic torsion of some prismatic bar with Gaussian material uncertainty and the corresponding reliability measures. This problem has been solved using the Constant Stress Triangular (CST) plane finite elements, the modified Newton–Raphson algorithm, whereas the first four probabilistic characteristics resulting from the iterative generalized stochastic perturbation method have been contrasted with these obtained with the crude Monte‐Carlo sampling and the semi‐analytical probabilistic approach. [ABSTRACT FROM AUTHOR]

The support system of heat, ventilation, and air conditioning (HVAC) in manufacturing plants plays an important role in the optimum efficiency of the production system. Failure to support adequate HVAC on the production floor will cause production defects, stoppage time, unplanned machine downtime, and other related issues. These issues highly affect those products that require dedicated temperature, pressure, and humidity, such as production clean rooms, food industries, and pharmaceutical products. Thus, this study aims to develop and apply the specific reliability assessment in an investigation of HVAC, focusing on the production of pharmaceutical products. In this study, the selected statistical quality tools have been applied in the evaluation and prediction of the HVAC failure and maintenance process. To ensure suitable tools and methods will be applied, a systematic approach has been developed consisting of six steps towards effective maintenance of the HVAC system. The main purpose of the application quality tools such as check sheet, Pareto chart, scatter plot, and probability plot was to evaluate the historical failure data and then to predict the potential future failure through analysis of Mean Time Between Failure (MTBF) in steps 1 to 4. To ensure the developed approach successfully implemented the Failure Mode and Effects Analysis (FMEA) in step 5, the corrective action plan was suggested in step 6. Once the systematic approach of HVAC preventive maintenance had been successfully developed, it was then implemented in the selected production floor of the pharmaceutical industry. The execution of this developed systematic approach to predicting the HVAC resulted in an increase in the total MTBF from 529.50 minutes to 778.23 minutes. It showed an improvement of 47% after the implementation of the developed systematic approach. The obtained results showed that reliability assessment was important for the optimization of production efficiency. [ABSTRACT FROM AUTHOR]

This manuscript presents a novel approach to the reliability of guyed transmission line towers (TLTs) considering complete geometric and material non-linear analyses, bolt slippage effects, and progressive collapse simulation, using active learning Kriging to handle the computational burden. In the existing literature, such issues have been considered separately, with the main focus on self-supporting towers. Herein, novelty arises from the combined consideration of the above and the addressing of guyed TLTs. Yet, because the wind speed variability is much higher than those related to the mechanical model, a modified initial sampling plan strategy is successfully proposed to reduce the computational time required. The application case-study addressing long transmission lines in midwest Brazil is also relevant, considering the recent updating of design wind speeds, with increases of up to 25%. Failure probabilities obtained using code resistance models indicate the need for tower retrofitting. However, the more accurate assessment via collapse simulation shows that the support is still safe, even for the updated wind speeds. Results demonstrate the accuracy and efficiency of the proposed analysis framework and show the importance of using accurate numerical models in retrofit evaluations. [ABSTRACT FROM AUTHOR]

This paper addresses the need for accurate and objective safety assessments of bridges in Brazil taking into account the increase in traffic load and limited funding for infrastructure. In Brazil, the usual method of ensuring bridge safety involves strengthening based on design codes using deterministic analysis and partial safety factors. This method leads to an increase in the maintenance cost of the bridges. Structural reliability assessment, a higher-tier assessment method, is recommended globally for assessing the safety of existing bridges beyond current deterministic methods. This paper presents a study that evaluates the structural safety of existing road bridges in Brazil using the semi-probabilistic method defined in Brazilian standards and the full probabilistic method. The study selected six case studies of existing reinforced and prestressed concrete bridges to evaluate their structural safety concerning current normative loads. The results suggest that the partial safety factors defined in the Brazilian standards are consistent with international standards. The reliability analysis can guarantee the bridges' safety, even with increased traffic loads, without additional costs for structural strengthening. Therefore, the paper highlights the importance of promoting reliability analysis in Brazil to ensure the safety of existing bridges and promote cost-effective and objective safety assessments. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)