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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]

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]

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]

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]

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]

In this paper, a reliability evaluation method based on the hidden Markov model (HMM) considers the influence of degradation trajectory fluctuation. To address the problem in which the performance degradation trajectory modelling of complex systems is limited by sampling time points, this method constructs an approximate degradation trajectory model based on effective discrete data points through a radial basis function to realize the degradation data expansion of small samples. Then, the average degradation rate is defined, and the fluctuation degree of the degradation trajectory is quantitatively evaluated to obtain the evaluation function of the fluctuation degree of the degradation trajectory. In addition, to determine the relationship between the fluctuation degree of the degradation trajectory and the operating state of the system, the method combines HMM with the evaluation function of the fluctuation degree of the degradation trajectory and obtains the transition probability matrix of different operating states of the system affected by the fluctuation degree of the degradation trajectory. On the basis of the probability matrix of the failure state, the reliability change curve of the system is derived. Finally, the effectiveness of the proposed method is verified via an example of a computer numerical control machine tool. [ABSTRACT FROM AUTHOR]

Two‐level balanced systems with common bus performance sharing (TLBS‐CBPS), represented by power battery systems, play an important role in various industries. Where the two‐level balance refers to when the system is unbalanced, redistributing performance between modules using a common bus for the intermodule balance, followed by the in‐module component performance redistribution to achieve global balance, that is, the performance differences among all components are within the balance degree threshold. However, the component performance has epistemic uncertainty due to measurement limitations, and the distance‐dependent transmission loss incurs during the performance redistribution. These problems make the reliability evaluation of TLBS‐CBPS challenging. To address these issues, a reliability evaluation method for TLBS‐CBPS is proposed by considering two‐level balance, epistemic uncertainty, and transmission loss. First, the system model is constructed from working modes, and the two‐level rebalancing process is described as three nonlinear programming problems. Then, based on the Dempster–Shafer evidence theory, the belief universal generating function (BUGF) method is proposed to evaluate the uncertainty of the system reliability. To improve the computation efficiency, a simplified BUGF algorithm is given through merging, judging, labeling, and expanding realizations. Finally, two examples of lithium‐ion battery packs demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The degradation process analysis is the basis of the system reliability assessment. Too few degradation samples will affect the accuracy of the reliability analysis. So, it is necessary to expand the degradation data and construct the degradation prediction model. This paper proposes an SVAE‐GRU‐based degradation generation and prediction model to handle the issues. New degradation data are generated by combining the Stacked Variational Autoencoder (SVAE) and Gated Recurrent Units (GRU) in each time step, which can improve the learning effectiveness of the degradation features. The GRU and the Multi‐layer perceptron calculate the mean and the variance of the degradation distribution for each timestamp, the deeply heterogeneity and temporal feature of the original degraded samples are learned. Then the degradation amount of different samples are predicted. The optimal hyper‐parameters of the SVAE‐GRU are obtained by the grid search method. To verify the effectiveness of the proposed method, three datasets and four methods are used for experiment comparison in this paper. The actual example indicate that the SVAE‐GRU method has the best effect on the degradation generation and prediction. [ABSTRACT FROM AUTHOR]

Modern complex engineering systems oftentimes possess hierarchical structures which can be physically divided into several levels. Traditional reliability assessment methods were conducted using field data or time‐to‐failure data. For a hierarchical system, different levels' reliability‐related data from monitoring or observations can provide additional information to update the system's reliability. However, with the limitation of sensing and monitoring technologies and vague judgments from experts, the reliability data inevitably contain epistemic uncertainty. Properly fusing the imprecise data can still improve the accuracy of system reliability assessment. In this article, we propose a reliability assessment approach based on the evidential network (EN) model to update the hierarchical systems' reliability by aggregating the system‐level imprecise knowledge. First, the evidence theory is applied to quantify the epistemic uncertainty associated with the imprecise system‐level reliability data. Then, an EN model is created to realize the uncertainty propagation and fusion of imprecise data. Finally, at any particular time instant, the system reliability can be updated with the aid of imprecise data based on the proposed EN model. The effectiveness of the proposed method is validated by a real engineering case of a wind turbine system. Overall, the results shown from the case study support that the proposed method can update the reliability of hierarchical systems by using system‐level imprecise data. [ABSTRACT FROM AUTHOR]

Expert knowledge is an important information source for reliability assessment of those systems with limited time‐to‐failure data. For a better understanding of the degradation profiles of systems, multiple experts are oftentimes invited to express their judgments and the associated uncertainties on the reliability‐related measures of the system. Evidential variables, as an alternative of uncertainty quantification, have been extensively used in expert systems to quantify the epistemic uncertainty of the elicited expertise. When eliciting the reliability‐related information by evidential variables, experts only need to express the possible ranges of reliability‐related measures and their associated probabilities. Such a type of information well caters to the experts' elicitation process. In this article, an evidential network (EN)‐based reliability assessment method is put forth by fusing multi‐source evidential information. The proposed method mainly contains three steps. In the first place, the multi‐source evidential information related to all components is elicited from experts in the form of evidential variables. Next, the evidential variable of the component reliability is assessed via a constrained optimization model by treating all pieces of multi‐source evidential information as constraints. The component reliability results are transformed into pieces of mass functions of the components' states under the theory of belief functions. The system reliability‐box and the reliability‐box over time are, therefore, calculated by the EN model by inputting all mass functions of components' states. A pipeline system and a chip cutting system are exemplified to examine the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Goal‐oriented (GO) methodology is a graphic inductive method mapping the system structure, its function constitute, characteristics, and working principle visually based on decision tree. Recently, the studies on reliability technologies for GO method of complex systems have been gotten a lot of attention by scholars because of the obvious advantages of modeling and analysis power. Indeed, the GO method has been applied in reliability and safety analysis of various areas, such as transportation and power systems, to prove its value. Thus, this paper provides a brief review of the GO method from aspects of providing its development history and its engineering applications and expounding its development of the theory of the GO method. At last, the future directions of GO method are discussed. [ABSTRACT FROM AUTHOR]

In reliability analysis, degradation test has been recognized as an effective method for high reliable products and complex systems when key performance indicators can be observed. Then, a reasonable degradation model becomes a key issue to guarantee a reasonable reliability assessment. Motivated by practical needs, this paper proposes a novel two‐stage degradation model considering the different dispersity regulations corresponding to the two stages. A maximum likelihood estimation (MLE) method for unknown parameters is established, and an initial guess procedure is given to improve the efficiency of optimization algorithm. Then, the reliability inference regarding the product population is discussed. A comprehensive simulation study is conducted to validate the proposed approach where the two‐stage Wiener process model is adopted as a reference for a better understanding. Finally, the constructed model is further verified by two real applications. Comparative results clearly demonstrate the reasonability and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

For a period of mission time, only zero‐failure data can be obtained for high‐quality long‐life products. In the case of zero‐failure data reliability assessment, the point estimates and confidence interval estimates of distribution parameters cannot be obtained simultaneously by the current reliability assessment models, and the credibility of the assessment results may be reduced if they are obtained at the same time. A new model is proposed for consistency problem in this paper. In the proposed model, the point estimates of reliability can be obtained by the lifetime probability distribution derived from matching distribution curve method, while the confidence interval estimates of reliability can be obtained by using new samples generated from the lifetime probability distribution according to parameter bootstrap method. By analyzing the zero‐failure data of the torque motors after real operation, the results show that the new model not only meets the requirements of reliability assessment but also improves the accuracy of reliability interval estimation. [ABSTRACT FROM AUTHOR]

Abstract: The network theory is widely applied to improve the reliability of a complex electromechanical system. In this application, system reliability assessment with network theory has been paid a great deal of attention. Because of instrument malfunctions, staff omissions, imperfect inspection strategies, and complex structures, field failure data are often subject to interval censoring, making the holistic reliability assessment becomes a difficult task. Most traditional methods assume reliability of critical components or partial reliability as system reliability, which may cause a large bias in system reliability estimation. This paper proposes a novel method to evaluate and predict the system reliability of a complex electromechanical system subject to the insufficient fault data problem from a network perspective. First, the system modeling based on network theory is developed to describe the topology of a holistic system. Second, interval‐valued intuitionistic hesitant fuzzy number is proposed in order to solve insufficient data for single component. Then, a new measure—comprehensive reliability—that can reflect the reliability of nodes in combination with functional properties and topological properties, which are formulated by failure data and network model, respectively, is constructed for system reliability assessment. Subsequently, an improved system reliability model based on percolation theory is given in terms of comprehensive reliability of nodes. Finally, to verify the effectiveness of the proposed method, a simulation and a real case study for traction system are implemented. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we presented a continuous‐time Markov process‐based model for evaluating time‐dependent reliability indices of multi‐state degraded systems, particularly for some automotive subsystems and components subject to minimal repairs and negative repair effects. The minimal repair policy, which restores the system back to an “as bad as old” functioning state just before failure, is widely used for automotive systems repair because of its low cost of maintenance. The current study distinguishes with others that the negative repair effects, such as unpredictable human error during repair work and negative effects caused by propagated failures, are considered in the model. The negative repair effects may transfer the system to a degraded operational state that is worse than before due to an imperfect repair. Additionally, a special condition that a system under repair may be directly transferred to a complete failure state is also considered. Using the continuous‐time Markov process approach, we obtained the general solutions to the time‐dependent probabilities of each system state. Moreover, we also provided the expressions for several reliability measures include availability, unavailability, reliability, mean life time, and mean time to first failure. An illustrative numerical example of reliability assessment of an electric car battery system is provided. Finally, we use the proposed multi‐state system model to model a vehicle sub‐frame fatigue degradation process. The proposed model can be applied for many practical systems, especially for the systems that are designed with finite service life. [ABSTRACT FROM AUTHOR]

Availability of a system is a crucial factor for planning and optimization. The concept is more challenging for modern systems such as robots and autonomous systems consisting of a complex configuration of components. In this paper, a reliability evaluation framework is developed for a system of binary state autonomous robots in an automated manufacturing environment. In this framework, the concepts in functional block diagram, table of truth, and sum of state are employed simultaneously to develop a binary state reliability model. Due to inefficacy of the method for larger number of components involved in complex systems, an extension of the Bernoulli trials is proposed. In an implementation study, the effectiveness and computational efficiency of the proposed method are illustrated. In addition, an analysis on the failure rate using the maximum likelihood estimation and confidence interval is reported. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the issue of degradation modeling and reliability assessment for products under irregular time-varying stresses. Conventional degradation models have been extensively used in the relevant literature to characterize degradation processes under deterministic stresses. However, the time-varying stress, which may affect degradation processes, widely exists in field conditions. This paper extends the general degradation-path model by considering the effects of time-varying stresses. The new degradation-path model captures influences of varying stresses on performance characteristics. A nonlinear least square method is used to estimate the unknown parameters of the proposed model. A bootstrap algorithm is adopted for computing the confidence intervals of the mean time to failure and percentiles of the failure-time distribution. Finally, a case study of lithium-ion cells is presented to validate the proposed method. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Since the 1990s, the increasing deployments of networked automation systems led to increased manufacturing productivity, improved interchangeability of devices from different vendors, facilitated flexibility and reconfigurability for various applications and improved reliability, while reducing installation and maintenance costs. However, the reliability of a network has great impact on the reliability of a networked automation system. This paper presents a novel network reliability assessment method that provides diagnostic and prognostic information for DeviceNet. This work proposes a hybrid network error analysis method using combined physical and datalink layer features to provide complete communication log information. Furthermore, a network/node time to failure (bus-off) prediction algorithm was developed based on the analysis of the patterns of the interrupted packets on the network. The method developed in this study can be used for network reliability evaluation and diagnosis, facilitating better network maintenance decision making. A laboratory testbed was constructed and the experiments on network and node time to failure were conducted to demonstrate the concept. Experimental results show that the proposed method can fully reconstruct the communication log, and predict the network/node bus-off time successfully. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The paper examines the research needed to advance the science of Q ∓R and in particular looks at that required to overcome the present crisis in reliability prediction and assessment. Areas requiring special attention are methods for the objective grading of product and organizational quality, research into human error causes and prevention, better modelling of reliability for prediction purposes, the discovery and use of unreliability diagnostic parameters, and better understanding and application of accelerated testing. Accelerated testing, screening and reliability prediction should all be more failure- mode-specific than they are currently. Reliability physics should pay increased attention to the precise effect material and geometric defects have on device life. [ABSTRACT FROM AUTHOR]