Search

We evaluated how the number of response options affects the psychometric properties of the Big Five Inventory-2 (BFI-2). Using two large samples collected from a market research company (N s = 893 and 1,213), we tested how different response options of the BFI-2 influenced scale score distributions, internal consistency estimates, convergent validity correlations, and criterion validity correlations. Results suggest that score distributions were impacted by the number of response options such that ceiling and floor effects were more common when using two or three response options than when using more options. Estimates of Cronbach's alpha were generally lower with fewer scale points as compared with more scale points, but these effects disappeared when ordinal alpha was used. There were no systematic effects of response options on convergent validity and criterion validity correlations. Given these results, there seems to be few psychometric reasons for deciding whether to administer personality items with five, six, or seven scale points. [ABSTRACT FROM AUTHOR]

The moment method is commonly used in reliability analysis, in which the maximum entropy method (MEM) and polynomial fitting (PF) have been widely used due to their advantages in accuracy and efficiency, respectively. In this paper, we propose a novel reliability analysis method by combining MEM and PF. The probability density function is preliminarily estimated using the fractional moment maximum entropy method (FM-MEM), based on which PF is then used to further improve the accuracy. The proposed method can avoid the phenomenon of the negative probability density and function oscillations in PF effectively. Moreover, the order of the exponential polynomial in the FM-MEM is adaptively selected in the preliminary solution calculation process. An iterative process for the number of exponential polynomial terms is also proposed, using the integral of the moment error function and the integrals of the local and global negative probability density as the convergence criteria. Four numerical examples and one engineering example are tested, and the results are compared with those of the Monte Carlo simulation and the classical FM-MEM results, respectively, demonstrating the good performance of the proposed method. [ABSTRACT FROM AUTHOR]

The study investigates an earth slide type rain-induced landslide that occurred at Madithorai, Nilgiris District, India in 2009. It consists of detailed field investigations such as topographical survey and borehole investigation, laboratory and numerical investigations. Based on the topographical survey the slope was modelled in SEEP/W programme to perform the uncoupled transient seepage analysis. The cumulative rainfall of 586 mm was given as an input for a duration of 5 days that caused landslide at Madithorai. The results of transient seepage analysis were used to determine the stability of the slope based on the infinite slope method for unsaturated soil. Based on the analysis the failure mechanism of Madithorai landslide was observed. The influence of antecedent rainfall using three different idealized 5-day antecedent rainfall was studied. A threshold intensity was established from the different 5 days antecedent rainfall and the corresponding factor of safety which can be used as for landslide early warning. The factor of safety from deterministic analysis was greater than one which does not imply that the slope is stable. Therefore, the performance of the slope was studied based on the reliability index and probability of failure using the Monte Carlo simulation. The results highlight that the slope is in hazardous condition with a reliability index of 1.15 and the probability of failure of 11.7%. Hence, it is recommended to adopt a landslide early warning system and suitable remedial measures at Madithorai location. [ABSTRACT FROM AUTHOR]

The pivotal problem in reliability analysis is how to use as few actual assessments as possible to obtain an accurate failure probability. Although adaptive Kriging provides a viable method to address this problem, unsatisfied Kriging surrogate accuracy and reliability modeling samples often lead to an unacceptable computing burden. In this paper, an adaptive optimized Kriging combining efficient sampling (AOK‐ES) is proposed: first, to enhance the Kriging approximation ability, a high‐fidelity optimized Kriging model (OKM) is established; further, to ensure the samples quality of OKM modeling and reliability calculation, an improved Latin hypercube sampling method and an optimized importance sampling approach are developed correspondingly. Six different types of case studies demonstrate the superiority of the proposed AOK‐ES. The analysis results demonstrate that the proposed AOK‐ES holds the potential to reduce computing cost while ensuring accuracy. [ABSTRACT FROM AUTHOR]

Abstract.This article primarily considers the statistical inference of the scale parameter, shape parameter, and reliability of the generalized logistic distribution in both censored and uncensored cases. For the progressively Type-II censored and complete samples, the frequentist method is utilized to construct asymptotic confidence intervals, and the Bayesian inference method is employed for constructing the point estimators as well as posterior credible intervals. Then the generalized fiducial method is applied to construct the fiducial point estimators and the fiducial confidence intervals. Furthermore, the non parametric generalized fiducial method is introduced to estimate the survival function. Simulation results demonstrate that the generalized fiducial method consistently outperforms other methods in terms of mean square error, average length, and empirical coverage. Finally, two real datasets are used to illustrate the proposed methods. [ABSTRACT FROM AUTHOR]

As an essential mechanical component, a rolling bearing can exhibit multiple failure modes that may occur independently or in correlation with one another. A reliability analysis method that meticulously accounts for the interdependencies among various bearing failure modes is presented in this paper. The examination of wear and fatigue failure mechanisms in rolling bearings is carried out using the Physics of Failure (PoF) approach. By considering the influence of uncertain variables, the limit state functions for individual failure modes are formulated through the application of stress‐strength interference theory. In the context of wear failure, the limit state function is derived using working clearance as the characteristic quantity. On the other hand, the limit state function for fatigue failure is constructed with a focus on fatigue damage accumulation. The Copula function is used to characterize the relationship between wear failure and fatigue failure, and a reliability calculation model for rolling bearings is developed, considering the correlation between these failure modes. Ultimately, the proposed method is utilized to assess the reliability of bearings under two different sets of test conditions. The feasibility of this method is confirmed through test data, demonstrating its effectiveness in predicting bearing reliability. Through the application of this method, engineers can optimize bearing size parameters, select appropriate initial clearances, and enhance the reliability design of bearing. [ABSTRACT FROM AUTHOR]

In this study, a new generalization of Samade distribution has been proposed. The weighted Samade distribution (WSD) is a new distribution designed for modeling real-life data. The survival function, hazard function, moments, moment-generating function, order statistics, and entropies of this distribution have all been determined. The parameters of the suggested model are estimated using the maximum likelihood estimation technique. Finally, the helpfulness and application of the distribution have been demonstrated by lung cancer data. [ABSTRACT FROM AUTHOR]

In this article, we proposed a new probability distribution called length-biased Chris-Jerry distribution in which length-biased distribution is a specific case of weighted distribution. Also, we determined the structural properties of length-biased Chris-Jerry distribution and the unknown parameters were estimated by maximum likelihood estimator and tested by likelihood ratio test. Finally, we emphasized a real lifetime data set to reveal how the proposed distribution worked in it. [ABSTRACT FROM AUTHOR]

With the constant progress made in the development of modern industrial technology, more and more highly integrated complex systems have emerged, whose reliability is often enhanced by setting system redundancy. When the reliability analysis is conducted on a complex system with redundancy, however, the failure of the redundancy management function hinders the spare parts from being converted as normal, which is called imperfect coverage (IPC). If the impact of IPC is ignored, the results of reliability analysis would be rendered inaccurate. Therefore, a multi-state Takagi–Sugeno dynamic fault tree (T–S DFT) analysis which considers IPC is proposed in this study to improve the accuracy of reliability analysis for the complex multi-state dynamic system. Firstly, the description rules of multi-state Takagi–Sugeno (T–S) dynamic gates are given. Secondly, the input and output of the warm spare (WSP) gate with IPC are analyzed to calculate the probability density function of the output of the WSP gate which considers IPC. Compared with the classical distribution, phase-type (PH) distribution shows satisfactory generality and analytical characteristics, which makes it suitable for dealing with complex random problems. Therefore, the life distribution of components is assumed to be PH distribution. Furthermore, the importance measures of multi-state T–S DFT analysis are presented. Finally, the reliability and importance measures of a radio station system are calculated to provide reference for identifying weak links, optimizing reliability design and maintaining operation. [ABSTRACT FROM AUTHOR]

From the perspective of accuracy, efficiency and versatility, calculating statistical moments for different forms of performance functions in a unified way remains a challenge, and their accuracy also considerably affects the results of subsequent reliability analysis. In this article, a new dimension-reduction model, termed as adaptive hybrid dimension-reduction model (AH-DRM), is derived to approximate the performance functions with different characteristics relatively accurately. Based on the AH-DRM, a highly flexible point estimation method (PEM), which has good versatility for the various forms of performance function, is developed for statistical moments estimation of stochastic systems. Subsequently, to illustrate the performance of the proposed method for different performance function forms, three examples, including weakly, moderately and strongly nonlinear benchmark examples, are investigated. The results indicate that the proposed method is versatile for each example and can receive desirable accuracy for evaluating the first-four central moments of the performance functions with high efficiency. Finally, the developed PEM is applied to reconstruct the probability density function of the performance function and calculate the reliability of structures with the aid of the saddlepoint approximation (SPA) method, and two practical engineering examples are used to verify the applicability of the proposed method in structural reliability analysis. [ABSTRACT FROM AUTHOR]

Water electrolysis is the primary production technology for clean hydrogen, and thus ensuring safe and reliable deployment and operation of electrolyzers is essential to global sustainability efforts. In this paper, electrolytic hydrogen production pathways and upcoming deployment capacities are briefly discussed. Then we examine the main risk and reliability challenges and analyze the state of the art in quantitative risk assessment (QRA) of electrolysis technologies. We identify gaps in electrolysis QRA studies and present recommendations to enable more robust risk and reliability analysis for these technologies. Recommendations and opportunities for research to fill these gaps include: developing and adopting a system reliability approach in electrolysis risk analysis, developing and disseminating detailed electrolysis system design documentation, developing reliability data collection and analysis methods and tools, conducting importance measures analysis, conducting uncertainty analysis and propagation, developing electrolysis QRA standards, and creating programs to develop and educate the hydrogen risk and reliability workforce. • Examined state-of-the-art in QRA studies for water electrolysis technologies. • Identified main electrolysis risk and reliability assessment modelling approaches. • Identified five key gaps in the electrolysis QRA studies examined. • Synthesized seven research opportunities to enhance electrolysis QRA validity. [ABSTRACT FROM AUTHOR]

Along with the booming of intelligent manufacturing, the reliability management of intelligent manufacturing systems appears to be becoming more significant. Failure mode and effects analysis (FMEA) is a prospective reliability management instrument extensively utilized to manage failure modes of systems, products, processes, and services in various industries. However, the conventional FMEA method has been criticized for its inherent limitations. Machine learning can handle large amounts of data and has merits in reliability analysis and prediction, which can help in failure mode classification and risk management under limited resources. Therefore, this paper devises a method for complex systems based on an improved FMEA model combined with machine learning and applies it to the reliability management of intelligent manufacturing systems. First, the structured network of failure modes is constructed based on the knowledge graph for intelligent manufacturing systems. Then, the grey relation analysis (GRA) is applied to determine the risk prioritization of failure modes. Hereafter, the k-means algorithm in unsupervised machine learning is employed to cluster failure modes into priority classes. Finally, a case study and further comparative analysis are implemented. The results demonstrate that failure modes in system security, production quality, and information integration are high-risk and require more resources for prevention. In addition, recommendations for risk prevention and monitoring of intelligent manufacturing systems were given based on the clustering results. In comparison to the conventional FMEA method, the proposed method can more precisely capture the coupling relationship between the failure modes compared with. This research provides significant support for the reliability and risk management of complex systems such as intelligent manufacturing systems. [ABSTRACT FROM AUTHOR]

Temperature and humidity coupling has a more significant effect on the failure properties of bonded joints than a single factor, and there is not enough research on this. In this paper, joints bonded with strong toughness structural adhesives are selected for the experimental analysis of joints aged for 240 h, 480 h, and 720 h at temperatures of 40 °C and 60 °C and a humidity of 95% and 100%. The sequential double Fick's model was used to fit the water absorption of the joints, and the comparison yielded that the water absorption of the adhesive was in accordance with Fick's law. The quasi-static tensile tests revealed that the reduction in mechanical properties of the joints was positively correlated with the moisture content in the environment, while the competing mechanisms of post-temperature curing and hydroplasticization resulted in a slight increase in the failure strength and energy uptake of the aged joints, which is in agreement with the experimental results of the Fourier infrared spectroscopy. A combination of macroscopic failure sections and scanning electron microscope (SEM) images yielded that the failure mode of the joints changed from cohesive failure to interfacial failure with increasing ageing time. In addition, reliability analyses for the fatigue testing of joints are expected to provide guidance for the life design of bonding technology in the vehicle service temperature range. [ABSTRACT FROM AUTHOR]

A major challenge in structural reliability is that computational accuracy and efficiency are hard to balance, especially the time-variant hybrid reliability problem. To improve the reliability analysis efficiency under the condition that the accuracy is assured, this paper develops a new learning function named Regional Learning with Weighted Simulation (RLWS) to estimate the failure probability. The Kriging model is first employed as a response surface to fit the response of limit state function. An adaptive regional learning model as the core of the most probable point is then proposed to update the Kriging model, where the boundary of the updated region is determined by an attenuation function. Furthermore, based on the RLWS model, this paper develops a continuous maximin salp swarm algorithm to calculate the upper bound of the response value for time-variant hybrid performance function. Meanwhile, the Salp Swarm Algorithm is employed to calculate the lower bound of the response value. The Monte Carlo simulation can thus facilitate evaluation based on the final generated Kriging response surface. Several case studies are performed to test and validate the effectiveness of the proposed method and its applicability to practical engineering problems. [ABSTRACT FROM AUTHOR]

Analytical probability density function of acceleration random response for cubic nonlinear product package has seldomly been investigated due to the difficulty of solving nonlinear random problem and less attention paid to the acceleration response. In this paper, the analytical solution for the probability density function of linear package acceleration response under random vibration is obtained, and on this basis, the equivalent linearization method is introduced to further explore the approximate analytical solution of acceleration response probability density function for cubic nonlinear product package under random vibration. Some suggestions on packaging optimal design are given by analyzing the sensitivity of acceleration random response to the external excitation and system parameters, and the first-passage failure probability for the acceleration response of cubic nonlinear package under random vibration is analyzed. The results show that the equivalent linearization method can precisely predict the acceleration response probability density function for the cubic nonlinear package under certain conditions, and the applicable range of the method is given by the equivalent stiffness formula. Nonlinear characteristic parameter γ of the cubic package activates acceleration response a completely different trend from the quasi-linear system. Properly increasing the system stiffness and decreasing the damping ratio can effectively reduce the acceleration response strength of the product package. As the vibration time increases and the product fragility decreases, the first-passage failure probability of the product package increases. The analysis provides theoretical foundation and guidance for packaging optimization design. [ABSTRACT FROM AUTHOR]

Energy piles are gaining popularity worldwide as both pile foundations and ground source heat producers. While considerable research has been conducted on energy pile design and evaluation, implementing these approaches effectively remains challenging for engineers. This study presents a novel approach based on Load and Resistance Factor Design (LRFD) with First-Order Second-Moment (FOSM) reliability analysis and target reliability ( β T ) for energy pile design. Additionally, three soft computing techniques, namely 1D-CNN, LSTM, and Bi-LSTM, were employed to enhance the design process. To conduct this research, datasets from each model were analyzed using various statistical indices, including R2, RMSE, RMSLE, MAPE, BF, AARD, NSE, U 95 , t-stat, TIC, and PI. The results indicated that the Bi-LSTM model demonstrated superior performance for heating operations, with R2, RMSE, and RMSLE values of 0.975, 0.021, and 0.007 for training and 0.984, 0.019, and 0.006 for testing datasets, respectively. For cooling operations, the LSTM model exhibited the most suitable predictive capabilities, achieving R2, RMSE, and RMSLE values of 0.969, 0.022, and 0.008 for training and 0.982, 0.019, and 0.007 for testing datasets, respectively. Comparative analyses, including rank analysis, Williams plot, accuracy matrix, and Taylor's diagram, were performed to assess the accuracy of the proposed models. The results demonstrated that the Bi-LSTM model for heating and the LSTM model for cooling outperformed other soft computing models within the reliability-based LRFD approach for energy pile design. This research contributes to the advancement of energy pile design methodologies, providing engineers with a more efficient and reliable LRFD-based framework. The successful application of soft computing techniques further enhances the accuracy of predictions for both heating and cooling operations. Overall, the findings of this study hold significant implications for the design and evaluation of energy piles, contributing to sustainable and energy-efficient construction practices. [ABSTRACT FROM AUTHOR]

• The moment similarity is explained qualitatively and quantitatively. • The positional accuracy reliability at any position is calculated based on the moment similarity. • An optimal position with the lowest failure probability can be searched in a cubic space. A new positional accuracy reliability analysis method of industrial robots is proposed based on the statistical moment similarity of positional error. The first-two order statistical moments of positional error at some positions are accurately obtained through the differential kinematics method to reduce the computational cost of the proposed method. In practical engineering, the statistical moments of positional error can also be obtained from the limited experimental data. Then, the statistical moment similarity of positional error is quantified by the semivariance function. Besides, the first-two order moments of positional error at any position can be calculated according to the statistical moment similarity. Thus, the positional accuracy reliability analysis of any position can be realized by the first-two order moments according to the saddlepoint approximation method. Besides, an optimal position with the lowest failure probability of positional error can be searched based on the statistical moment similarity. The effectiveness of the proposed method is manifest through calculating the positional accuracy reliability and the optimal position with the lowest failure probability of a 6-degree-of-freedom (6-DoF) industrial robot in a cubic space. [ABSTRACT FROM AUTHOR]

A new approach is proposed to analyze the non‐Gaussian random vibration of hyperbolic tangent package. Firstly, the non‐Gaussian vibration noise of specified mean, variance, skewness, and kurtosis is developed by Hermite polynomial and verified by Gaussian mixture model theory. Secondly, next to analyzing an analytical and numerical response of the two degrees of freedom linear package, the acceleration response of the two degrees of freedom hyperbolic tangent package system under non‐Gaussian vibration excitation is obtained though the Hermite polynomial and Runge–Kutta method, and the effects of the external excitation and system parameters are investigated. Finally, on the basis of the first‐passage probability of the acceleration response, the reliability analysis method is introduced. The analysis provides a guidance to the vibration reliability analysis and packaging optimization design. [ABSTRACT FROM AUTHOR]

The combined high and low cycle fatigue (CCF) loading condition and random uncertainty exert a considerable impact on the design of turbine shafts. To enhance the fatigue life and reliability, this research proposes a CCF reliability analysis and optimization method for turbine shafts. A CCF fatigue reliability analysis framework is established, which focuses on the quantification of CCF loading characteristics and random uncertainty. The consideration of CCF loading characteristics contain the loading frequency ratio of high cycle fatigue (HCF) to low cycle fatigue (LCF), the stress amplitude ratio of HCF to LCF, as well as their interaction. The consideration of random uncertainty contains material, geometry and load, and a surrogate model‐based method is introduced to improve the quantification efficiency. Through the validation by comparing with experimental data and traditional methods, the proposed method is with higher accuracy and efficiency. By integrating the proposed fatigue reliability analysis method with design optimization, optimal design values for the turbine shaft were identified. This method theoretically extends the shaft's CCF life and provides practical engineering guidance for its reliability analysis and design. [ABSTRACT FROM AUTHOR]

Cyber‐Physical Systems (CPS) are at the forefront of the intersection between information technology and physical operations, revolutionizing industries such as communication, automation, control, and intelligent transportation. However, evaluating the reliability of CPS, especially in the presence of multilateral random attacks, poses a complex challenge. This paper introduces an inventive probabilistic modeling approach to enhance the analysis of CPS reliability under the influence of multilateral random attacks. We provide an overview of fundamental CPS concepts and their significant role in modern society, followed by a comprehensive exploration of multilateral random attacks. Our novel probabilistic modeling approach is presented as a means to improve the accuracy and robustness of CPS reliability assessments when faced with multilateral random attacks. Numerical experiments and simulations validate the effectiveness of our innovative approach, emphasizing its potential to fortify the reliability of Cyber‐Physical Systems. [ABSTRACT FROM AUTHOR]

This study delves into the factors that affect the reliability of satellite antenna deployment mechanisms. It employs the Kolmogorov–Smirnov (K‐S) test to quantify uncertainty characteristics, leading to the acquisition of pertinent stochastic parameters. Subsequently, three‐dimensional models and finite element models of the satellite antenna deployment mechanism are established using SOLIDWORKS and ANSYS software. Modal distribution, harmonic response analysis, random vibration analysis, and deployment process simulation are conducted. Parameterized modeling of the deployment mechanism's dynamics simulation process is performed, taking into consideration parameter uncertainty. Finally, a Kriging surrogate model is utilized to formulate an approximate expression between the factors influencing the reliability of the antenna deployment mechanism and its dynamic response. Based on the general stress‐strength interference theory, a reliability analysis model is constructed. Combined with active learning algorithms, this approach achieves efficient and precise calculations of the reliability of the satellite antenna deployment mechanism. [ABSTRACT FROM AUTHOR]

During the operational phase, complex systems are subject to various shocks, both random and continuous degradation. This coupling effect poses significant challenges for reliability assessment. The proposed method aims to provide a clear and objective evaluation of the system's reliability. In this paper, we propose a competitive failure reliability analysis method that considers the coupling effect of multiple shock processes and degradation processes. The random shocks are subdivided into external and internal shocks. The degradation increment is mainly influenced by external shocks, whereas internal shocks affect the degradation rate. The combined effect of these two shock processes accelerates the failure processes. Additionally, two different shock models are combined to create an extreme‐cumulative shock model to assess the impact of various shock processes and the degradation process on system reliability. The analysis examines the coupling effect between the degradation process and shock processes to explore the influence law between hard failure and soft failure. Subsequently, the reliability modeling of competitive failure processes is completed. Finally, practical case studies demonstrate the effectiveness and practicality of the proposed method. [ABSTRACT FROM AUTHOR]

Modeling and evaluation of a system reliability is a key issue in shock model analysis. In cumulative shock model, a system fails when the total damages caused by shocks exceeds the system failure threshold level. The reliability of a system with random failure threshold level under cumulative shocks is investigated in this work, considering the general dependency between inter‐arrival times of shocks and their damages magnitudes. Clustering, as one of the unsupervised machine learning methods, is utilized here by applying a genetic algorithm (GA) and particle swarm optimization algorithm (PSO) for its implementation. Phase‐type (PH) distributions and their properties are also applied for finding a system failure distribution functions, determining the convolutions, evaluating the integrals, and ultimately modeling the system reliability. An illustrative example is provided to demonstrate the implementation and effectiveness of the proposed modeling procedure and the suggested approach for computing the system reliability and remaining useful lifetime (RUL). Also, an example case study of X‐ray machine in healthcare industry is provided to demonstrate the application of the proposed model. Applying other machine learning methods for patterns recognition is an area for future research. [ABSTRACT FROM AUTHOR]

Several existing FRP-confined concrete strength models were evaluated by using 85 FRP‑concrete‑steel DSTC (double‐skin tubular column) axial compression test data as statistical samples. Finally, Teng’s model was selected for reliability analysis, and the optimal probability distributions of strength model error and resistance were determined. The reliability of DSTC was calculated using the JC method. The results show that the reliability index increases with the increase of steel tube strength, hollow ratio and steel ratio, while it is not significantly influenced by concrete strength. The increase in FRP confining stress leads to the decrease in reliability index. Based on the target reliability index of 3. 7, a new expression of resistance partial factor is proposed for DSTC. [ABSTRACT FROM AUTHOR]

In this manuscript, a new probability model named as Sine Power Rayleigh distribution (SPRD) is proposed using a Sine-G function as generator. Various statistical properties of this new distribution were investigated, including the survival function, hazard function, reverse hazard rate, cumulative hazard function, mills ratio, quantile function, moments, moment generating function, conditional moments, entropy, and order statistics. The parameters of the proposed distribution were estimated using the method of maximum likelihood estimation. To assess the model's versatility and applicability, we conduct analyses on two real life data sets. The outcomes affirm the superior performance of the newly proposed model SPRD as compared to existing models. [ABSTRACT FROM AUTHOR]

This paper introduces a novel probability distribution known as the Ejaz distribution (ED), which is characterized by two parameters. The study offers a comprehensive analysis of this distribution, including an examination of key properties such as moments, moment-generating functions, order statistics, and reliability functions. Additionally, the paper explores the graphical representation of essential functions like the probability density function, cumulative distribution function, and hazard rate function, enhancing our visual understanding of their behavior. The distribution's parameters are estimated using the widely accepted method of maximum likelihood estimation. Through real-world examples, the paper highlights the practical applicability of the Ejaz distribution, demonstrating its performance and relevance in diverse scenarios. [ABSTRACT FROM AUTHOR]

The integrated navigation system (INS), vital in aerospace and ocean exploration, faces challenges in reliability analysis due to its complex operational environment. Addressing this, the finite degradation structures (FDS) method was employed for a thorough examination of the multi-state integrated navigation system. Initial steps involve transforming component fault modes into a state space using the failure mode and effect analysis ( FMEA) method. Subsequently, an abstraction operation establishes the relationship between component states and system states, forming the required FDS model. This model serves as the basis for critical scenario analysis and calculation of reliability indicators. Through this systematic approach, the occurrence probability of various system states can be computed and the sensitivity of individual component states can be assessed. The identifications of the weak points and the degradation boundaries of the system are well demonstrated with illustrative case studies. These results can be seen as insights to guide reliability decisions, including the development of system maintenance strategies and risk prevention schemes. [ABSTRACT FROM AUTHOR]

How to guarantee the stability of the cofferdam slope during and after construction is a difficult problem. In this article, a practical procedure is developed to implement probabilistic stability analysis for reinforced slope considering the spatial variability of soil strength. In the proposed procedure, the improved random limit equilibrium method is adopted to calculate the safety factor and particle swarm optimization is adopted to search critical sliding surface of slope. The influence of different reinforcement positions, lengths, and number of layers on the reliability of reinforced slope and critical sliding surface is studied. The results show the variability of soil, the length, location, and the number of layers of geosynthetics are important factors affecting probability of failure (Pf) of reinforced slope. The location and the number of layers of geosynthetic reinforcement play an important role in the statistical characteristics of FOS and slip mass volume. The reinforcement length has a greater impact on the safety factor. [ABSTRACT FROM AUTHOR]

In this paper, a probabilistic double linear damage accumulation model is proposed to characterize the uncertainty of damage accumulation using the statistics of material fatigue life. The probabilistic modeling framework is performed under normal distribution assumption of fatigue life. By applying a one‐to‐one probability density function transformation method, the probability distribution of critical damage and cumulative damage can be extracted and derived. The proposed probabilistic damage accumulation model is then used for fatigue reliability analysis by building the reliability formulas with respect to the cumulative damage and the critical damage under both constant and variable amplitude loadings. It treats the mean and variance of cumulative damage as time‐dependent values that dynamically vary with increase in usage life. The reliability can be easily calculated by limited data necessary from fatigue life samples. The applicability of the proposed method is demonstrated using the experimental data of two metallic materials under constant amplitude and two‐block loadings. The results show that the model predictions are in good agreement with the experimental observations under constant amplitude loading and L‐H loading sequence. In addition, the proposed method provides better predictive ability than the Miner rule under constant amplitude loading and H‐L loading sequence. Moreover, the predicted reliability plots can typically present a two‐stage fatigue characteristic during crack initiation and crack propagation periods. [ABSTRACT FROM AUTHOR]

The article analyses the evolution of mathematical models used in life cycle estimation. A comparison between the different mathematical models and their areas of applicability is proposed. This analysis is generally done in terms of costs. The importance of the article lies in the proposal of a new mathematical model for estimating life cycle of power substations, compatible with current and future requirements. The study findings reveal that an optimization model can be established using the life cycle assessment method to obtain a longer lifespan, but also to minimize costs (maintenance, repairs, replacements, etc.). In order to establish a mathematical model of the life cycle of electrical substations, it is necessary to study several important design parameters and decision factors for each stage of the life cycle. Even if the paper does not provide empirical results, it must prove that it is necessary to develop a multi-algorithm optimization framework to find an adequate solution. The life cycle must be modelled according to: equipment availability technological evolution, maintenance activities, risk management, environmental protection, human error, etc. The study helps further research to build, implement and validate a robust model with development possibilities depending on the needs of the researchers. [ABSTRACT FROM AUTHOR]

Summary: In the power electronics industry, reliability is of utmost importance to manufacturers. Power semiconductor devices are extensively utilized in multilevel inverters (MLIs), which results in increased failure probability. MLI structures have a significant impact on the system's overall reliability. MLIs with a high degree of reliability reduce system maintenance costs and improve efficiency and the life of the entire system. This paper presents a comprehensive evaluation of five‐level packed U‐cell (PUC5) inverter, modified five‐level PUC (MPUC5) inverter, and their fault‐tolerant (FT) variants with respect to reliability, degree of fault tolerance, power loss and efficiency, and cost. A more accurate reliability evaluation method is used in this work in which failure rates of each component of inverter topologies under healthy and post‐fault conditions are calculated. Hence, this method provides more accurate reliability function of an MLI topology as compared to other methods in which failure rates are assumed to be same for all switches. Typhoon HIL‐402 emulator is used to demonstrate the FT capabilities of topologies in hardware‐in‐the‐loop (HIL) environments. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) charging architectures with two stages consist of rectification and a DC–DC conversion stage. Multi-level rectifiers are emerging as better alternatives in the rectification stage of EV charging applications. As they possess minimum voltage stress on switches, better harmonic profile at the grid side, lower dv/dt stress, and higher voltage capability. In this article, a five-level rectifier in conjunction with a phase shift full bridge converter is implemented for EV charging applications. The rectifier has the inherent capacitor voltage balancing capability and maintains unity power factor at the source side under varieties of operating conditions. The reliability of power electronic converters can be estimated by accessing the reliability of each component in converters. To establish the suitability of the implemented system for real-time EV charging applications, comprehensive performance analysis has been carried out including reliability assessment, the impact of parametric variations on reliability, calculation of losses and efficiency. The battery charger is mathematically modeled and validated experimentally in laboratory. The comparative analysis of the rectifier with state-of-the-art literature has been also carried out. [ABSTRACT FROM AUTHOR]

A binary-state m-out-of-r-within-k-out-of-n:G model is proposed and the reliability of the model is estimated with the help of Universal Generating Function (UGF). In the proposed system, the subsystems are connected in k-out-of-n:G type configuration. The subsystems are also a combination of m-out-of-r:G type. The elements of the subsystem consist of components connected in series. The components cannot be divided further and taken as an individual unit. The reliability function and absolute reliability of the proposed model are evaluated from the UGF technique. [ABSTRACT FROM AUTHOR]

Reliability analysis aims at quantitatively assessing the risks of structures and infrastructure systems considering various sources of aleatory or epistemic uncertainties. This concept can be interpreted as reliability updating (RU) to track the change of risks, fusing new data emerged in the operation period. Optimal allocation of resources can be therefore prepared for reasonable maintenance and rehabilitation. In the state-of-the-art method for RU with equality information, auxiliary random variables are introduced to transform the problem into an inequality one. However, the joint events derived in aforementioned approach are typically very rare, which can be computationally cumbersome via simulation techniques. To enhance the performance of RU with equality information, this paper proposes a new reliability updating approach that decomposes the joint event term into two separate multipliers and subsequently computes conditional probability through subset simulation (SS). Moreover, this work analyzes the statistical properties for RU through SS. An adaptive manner is proposed to dynamically adjust the batch sample size for each subset so that the consistency of simulation results can be ensured. Compared to past SS-based reliability updating approaches, the proposed method is computationally easier to handle and substantially more robust. Three numerical examples together with a practical application of structural health monitoring are investigated to demonstrate the computational superiority of the proposed method. [ABSTRACT FROM AUTHOR]

System reliability directly affects the realization of software‐intensive system functions, which rely on both hardware and software. The operating status of hardware and software affect each other, and the continuous accumulation of their degraded performances will accelerate the occurrence of failures. However, the interaction relationship between hardware and software is often overlooked in most current research. In this paper, a new reliability analysis method is proposed for multi‐state software‐intensive systems with degradation accumulation effect and information transmission attenuation based on goal oriented (GO) methodology. First, a new function GO operator is created, and its operation formula is derived with the aid of Markov process theory. To quantify the impact of degradation accumulation on availability, non‐negative constant and random variable critical thresholds are set for the degradation state. Then, an effective optical power loss model is established to analyze the reliability of information transmission in the presence of attenuation. Next, the reliability analysis framework for multi‐state software‐intensive system is established based on the GO method. Finally, the proposed reliability analysis method is applied to the example of meter and decameter wavelength radio heliograph to demonstrate the applicability and feasibility of our work. The proposed model provides a more accurate reflection of the system reliability under actual operating conditions. [ABSTRACT FROM AUTHOR]

• An analytical method solving SIFs of asymmetric circular and elliptical hole-edge cracks is proposed. • The impact of multiple interactive cracks on SIFs is studied. • Safe cracks tip distance is advised based on the reliability analysis of an engineering case. In this paper, a successive conformal mapping technique is used to propose an analytical method for solving the stress intensity factor (SIF) of asymmetrical hole-edge cracks on the basis of Muskhelishvili's complex variable function analysis method. Based on this analytical solution, the superposition method is used to get the SIF under the interaction of multiple cracks. Subsequently, according to the plastic collapse criterion, the residual strength under the interaction of two collinear asymmetric elliptical hole-edge cracks with different sizes on the side rail of a dumper's frame is analyzed through the method established. The failure probability of the plate part is obtained by the Kriging meta-model. This study provides a theoretical guide for the operation and maintenance plan of plate parts with multiple cracks. [ABSTRACT FROM AUTHOR]

The classical reliability analysis methods, due to the ever-increasing complexity of engineering structure, may lead to higher and higher calculation errors and costs. The adaptive surrogate-model-based reliability evaluation method strikes a desirable balance between computational efficiency and accuracy, making it a prevalent technique in the domain of reliability evaluation. Learning function is the core of this reliability evaluation method. In this study, a novel learning function is proposed to adaptively choose the best update sample. This learning function does not depend on the prediction variance provided by the Kriging model. Therefore, this learning function is not limited to the Kriging model. In theory, it can be combined with different surrogate models. Four comparative cases are used to illustrate the computational efficiency and accuracy of the proposed method, including series system case with four branches, highly nonlinear two-dimensional numerical example, and two practical engineering case. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'. [ABSTRACT FROM AUTHOR]

This paper introduces a new cubic transmutation of the inverse Weibull distribution, known as a cubic transmuted inverse Weibull distribution. The model is thought to be useful for the analysis of complex life data, modeling failure times, accessing product reliability, and many other fields like economics, hydrology, biology, and engineering. Some statistical features of the proposed distribution are explored. These include moments, generating functions, quantile functions, reliability functions, and hazard rate functions. The distribution of order statistics for the proposed cubic transmuted inverse Weibull distribution is also studied. The maximum likelihood estimation approach is used to estimate the model parameters. The effectiveness of the estimation is investigated through extensive simulation study. The suitability of the proposed distribution has been studied by using five real-life datasets. It is found that the proposed distribution is the most suitable fit for the used data sets. [ABSTRACT FROM AUTHOR]

A multi-fidelity integration method is proposed to analyze the reliability of multiple performance indicators (MPI) for industrial robots. In order to high-fidelity mapping the performance of industrial robots, a unified multi-domain model (UMDM) is first established. The contribution-degree analysis is then used to classify the input random variables into interacting and non-interacting ones. Thus, the high-dimensional integration of reliability analysis is separated into a low-dimensional integration and multiple one-dimensional integrations in an additive form. Here, the low-dimensional integration consisting of the interacting variables is calculated using the high-precision mixed-degree cubature formula (MDCF), and the computational results are treated as high-fidelity data. The one-dimensional integration consisting of non-interacting variables is then computed by the highly efficient five-point Gaussian Hermite quadrature (FGHQ), and the computational results are named low-fidelity data. A multi-fidelity integration method is constructed by fusing the high-fidelity data and the low-fidelity data to obtain the statistical moments of the MPI. Subsequently, the probability density function and the failure probability of the MPI are estimated using the saddlepoint approximation method. Finally, some representative methods are performed to verify the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Water is a necessary component of urban structures and a source of life. Nowadays water distribution system's performance might decline for various reasons, resulting in water waste, hence, to maintain a reliable water distribution network, assessing each water distribution study's validity is crucial. Therefore, this research has focused on using durability assessment to evaluate a water supply system. A water distribution network includes an overhead tank and nodes, pipelines, and nodes. The hydraulic properties of every node as well as pipe, including pressure and flow, are calculated utilizing EPANET. This research evaluates the reliability analysis based on the benchmark data's various diameter of pipes, such as 0.6 m, 0.4 m, 0.3 m, 0.2 m, and 0.15 m, usage. Finally, this research analyses the reliability of each set of diameter pipes in the water supply system. [ABSTRACT FROM AUTHOR]

Despite the developing technology, physical works are carried out by employees in many sectors. The working postures that the employee is exposed to affect her/his performance in the short term, and in the long term, it returns to the employee as Musculoskeletal Disorders (MSDs). MSDs has negative material and moral effects on workers, employers, and the country's economy. Therefore, ergonomic studies which aim to adapt human to work and work to human have importance. Working posture analyses are utilized to detect and to improve inappropriate postures in this context. REBA (Rapid Entire Body Assessment) is a working posture analysis method which enables to state risks rooted from inappropriate working postures as numerically. In this study, it has been aimed to evaluate reliability of analysis results obtained from REBA method. Within this scope, 200 working postures belonging to different work groups selected from a company operating in the automotive subsidiary industry were analyzed by 4 different raters by using REBA method. These scores were assessed with intra-rater (same observer in different time zones reviews) reliability analysis and inter-rater (evaluations belonging to different observers) reliability analysis. Inter-class reliability analysis results which made in %95 confidence interval shows that A score ((Intraclass Correlation Coefficient - ICC=0,871), C score (ICC=0,844) and final REBA score (ICC=0,852) are high level of reliability. However, B score (ICC=0,704) is medium level of reliability. According to intra-class reliability analysis results which made in %95 confidence interval REBA method analyses working postures with high level of reliability. [ABSTRACT FROM AUTHOR]

• Probabilistic analysis of soil slope of heavy-haul railway embankment. • Probabilistic analysis of a 12.293 m high soil slope against bearing capacity failure. • Assessment of failure probability of a soil slope in seismic and non-seismic conditions. • Assessment of failure probability for different COVs of soil parameters. This study presented a probabilistic assessment of heavy-haul railway track using a high-performance computational model called multi-gene genetic programming (MGGP). A reliability analysis (RA) method based on MGGP and the first-order second-moment method (FOSM) has been proposed in this study. First, GP was used to map the implicit performance functions; therefore, arriving at GP-based explicit performance functions. Subsequently, the developed GP model was used to perform RA of a soil slope of heavy-haul railway track under both seismic and non-seismic conditions. Using the FOSM, soil uncertainties were mapped based on the concepts of probability theory and statistics, and a ready-made expression was developed. Simulated results demonstrate that the GP-based FOSM approach can predict the probability of failure (POF) of slope with rational accuracy. The probabilistic analysis against bearing capacity failure was also investigated in this study to ensure serviceability of the soil slope. Based on the outcomes, it can be deduced that the coefficient of variation of soil properties affects the POF of slope significantly. With the aid of the developed expression, the POF of the soil slope of heavy-haul railway track can be assessed rationally and efficiently. [ABSTRACT FROM AUTHOR]

The present work's main objective is to investigate the natural vibrations of the thin (Kirchhoff–Love) plate resting on time-fractional viscoelastic supports in terms of the Stochastic Finite Element Method (SFEM). The behavior of the supports is described by the fractional order derivatives of the Riemann–Liouville type. The subspace iteration method, in conjunction with the continuation method, is used as a tool to solve the non-linear eigenproblem. A deterministic core for solving structural eigenvibrations is the Finite Element Method. The probabilistic analysis includes the Monte-Carlo simulation and the semi-analytical approach, as well as the iterative generalized stochastic perturbation method. Probabilistic structural response in the form of up to the second-order characteristics is investigated numerically in addition to the input uncertainty level. Finally, the probabilistic relative entropy and the safety measure are estimated. The presented investigations can be applied to the dynamics of foundation plates resting on viscoelastic soil. [ABSTRACT FROM AUTHOR]

In this article, a two-parameter model called unit-Gompertz is studied over the unit interval. We describe its statistical characteristics such as uncertainty measures of Shannon, Tsallis, Renyi, Mathai-Houbold, Kumar and Verma, order statistics, quantile function, maximum likelihood estimation, factorial and characteristic function, moment generating function, and stress-strength analysis. The effectiveness of the studied model is demonstrated by the use of two datasets from the real life. The model has flexible hazard rate shapes, presents better performance and fits the data better than other usual models. [ABSTRACT FROM AUTHOR]

This manuscript focuses on the statistical properties and estimation methods of the exponentiated Suja distribution, which is characterized by two parameters: scale and shape. From a frequentist perspective, our primary emphasis is on estimation techniques. Additionally, we derive statistical and reliability characteristics for the model. We explore various estimation procedures, including order statistics, entropies, reliability analysis, and the maximum likelihood method. To assess the model's superiority and practical utility, we analyze real lifetime data sets. Overall, this study provides a comprehensive analysis of the exponentiated Suja distribution, offering insights into its statistical properties, estimation techniques, and real-life applications. [ABSTRACT FROM AUTHOR]

In this study, we introduce a novel class of distributions called the length biased Uma distribution. This distribution is a specific instance of the broader weighted distribution family, known for its versatility in various applications. We explore the structural properties of the length biased Uma distribution and propose a robust parameter estimation technique based on maximum likelihood estimation. To assess its efficacy, we apply the newly introduced distribution to two real-world datasets, evaluating its flexibility and performance in comparison to existing models. The results obtained demonstrate the potential of the length biased Uma distribution as a valuable addition to the repertoire of statistical distributions, offering valuable insights for a wide range of practical applications. [ABSTRACT FROM AUTHOR]

This paper presents a simple yet efficient approach for the reliability-based design of flexible pavements, based on the concept of quantile-values. The reliability-based approaches currently adopted in the pavement design guides are simplified techniques, based on an overall reliability factor. On the other hand, simulation-based probabilistic techniques, widely discussed in literature, can be computationally expensive and tedious to implement. The 'Simplified Effective Random Dimension Quantile Value Method' (Simplified ERD-QVM), adopted in this study, presents an elegant technique to bridge the gap between the precision of simulation-based probabilistic techniques and the efficiency of design guide approaches. The accuracy of this technique is verified through Monte Carlo Simulations for different pavement design scenarios. The main advantages of the method are (i) the significant savings in computational times for reliability-based design of flexible pavements without the use of surrogate models (ii) the use of simple techniques already familiar to practitioners who adopt the Mechanistic-Empirical Pavement Design Guide and (iii) it is a comprehensive reliability analysis approach that addresses the drawbacks of the currently used approaches, while capturing the effect of both design parameter and model uncertainty. The methodology for integrating the Simplified ERD-QVM with the existing flexible pavement design codes is also presented. [ABSTRACT FROM AUTHOR]

The limit state that typically governs the design of circular reinforced concrete water retaining structures is the limitation of leakage through cracks. Current design codes prescribe target crack widths in the range of 0.05–0.2 mm, depending on the design situation, to limit leakage to acceptable levels. Fairly high model uncertainty exists in the prediction of load‐induced crack widths and the beneficial effect of autogenous self‐sealing has not effectively been quantified, so that the achieved levels of reliability using current codified procedures are unknown. This research uses a reliability analysis previously developed by the authors to assess the level of SLS reliability achieved in tension‐governed reinforced concrete reservoirs covering much of the scope of practice. The SLS reliability performance is evaluated using a limit state of allowable versus predicted leakage, including a probabilistic quantification of the effect of autogenous self‐sealing, and the uncertainty present in the prediction of load‐induced crack widths. Many permutations of practical reservoir geometries are considered for four combinations of stabilization period and water tightness test time (leakage regimes) typically used in water tightness tests. The target crack width recommendations of EN 1992‐3 and Model Code 2010 are evaluated across the range of geometries and leakage regimes. The commonly used target crack width of wt=0.2mm, recommended by Model Code 2010 and many other codes, was found to be inadequate to achieve a target SLS reliability index of 1.5≤βt. The wt values from EN 1992‐3 showed better performance but were found to result in reliability indices frequently in excess of β=1.5, indicating that the wt values are perhaps too stringent. Results confirm that smaller target crack widths and longer water tightness test times increase the achieved reliability. However, even for a given target crack width and leakage regime, a substantial range of achieved reliabilities is observed depending on reservoir configuration. This shows that design to meet current target crack widths is not effective at meeting target reliability across the range of practical application. A suggestion is made to refine reservoir specific crack width targets to achieve target reliability more consistently. [ABSTRACT FROM AUTHOR]

Estimating the failure probability of rainfall-induced landslides is often challenging as the triggering mechanism is influenced by a number of parameters whose uncertainty is difficult to quantify and, in practice, is neglected. The reinforcing effect of vegetation on natural slopes adds to the complexity of the stability analysis. In this study, we present the application of a coupled hydro-mechanical model for the effect of plant roots on soil shear strength. First, a deterministic approach is adopted. Then, a reliability analysis of a root-reinforced slope subjected to rainfall is performed by considering the inherent variability of the soil and root properties. The probability of failure is estimated with machine learning surrogate models, which approximate the nonlinear relationship between constitutive parameters and slope displacements at different time steps. The machine learning algorithms are trained on a small dataset. The extreme gradient boosting is the best-performing algorithm with R2 ≥ 0.975 and is then employed to estimate the probability of failure on a larger dataset of one million datapoints with higher accuracy. [ABSTRACT FROM AUTHOR]

This article provides some optimal estimators for a parametric function θR, which arises in the study of reliability analysis involving several exponential populations. Let π 1 , π 2 , ... , π k be k (≥ 2) independent populations, where the population πi follows an exponential distribution with unknown guarantee time and a known failure rate. These populations may represent the lifetimes of k systems. Let θ i (t) be the reliability function of the ith system, and let θ (k) denote the largest value of θ i (t) 's at a fixed t. We call the system associated with θ (k) the best system. For selecting the best system, a class of natural selection rules is used. The goal is to estimate the parametric function θR, which is a function of parameters θ 1 , θ 2 , ... , θ k , and the random variables. The uniformly minimum variance unbiased estimator (UMVUE) and the generalized Bayes estimator of θR are derived. Two natural estimators δ N , 1 and δ N , 2 of θR are also considered. A general result for improving an equivariant estimator of θR is derived. Further, we show that the natural estimator δ N , 2 dominates the UMVUE under the squared error loss function. Finally, the risk functions of the various competing estimators of θR are compared via a simulation study. [ABSTRACT FROM AUTHOR]