Reliability Engineering & System Safety最新文献

{"title":"A large-scale group SLIM considering expert credibility under social network to estimate human error probabilities in the railway driving process","authors":"Jian-Lan Zhou ,&nbsp;Ya-Lun Zhou ,&nbsp;Ren-Bin Xiao","doi":"10.1016/j.ress.2024.110648","DOIUrl":"10.1016/j.ress.2024.110648","url":null,"abstract":"<div><div>The Large-scale Group Success Likelihood Index Method (LG-SLIM) can eliminate bias caused by a single expert in human error assessment. The traditional LG-SLIM uses trust degrees to cluster and reach a consensus. However, the existing clustering algorithms do not consider the trust degrees between a given pair of experts to be multiple and vary according to different evaluated tasks. Besides, the existing consensus models do not consider various combinations of the evaluated tasks and trusted experts’ professions when managing trust degrees and self-confidence. Therefore, the similarity-trust-based clustering algorithm is improved using the comprehensive trust degree integrated from diverse trust degrees concerning all evaluated tasks. Moreover, expert credibility is proposed to reflect the quality of the expert's evaluation results, determined by self-confidence and trust degree simultaneously according to various combinations of the expert profession and target task. Accordingly, under the social network derived from expert credibility, the incompatible outliers change their opinions by referring to the views of those with the highest expert credibility. Finally, the sensitivity experiment and comparative analysis verify the effectiveness of the proposed model. The proposed LG-SLIM model is useful for human error assessment when critical operations need many experts to obtain reliable and accurate results.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110648"},"PeriodicalIF":9.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-efficient non-iterative reliability-based design optimization based on the design space virtually conditionalized reliability evaluation method","authors":"Meng-Ze Lyu ,&nbsp;Jia-Shu Yang ,&nbsp;Jian-Bing Chen ,&nbsp;Jie Li","doi":"10.1016/j.ress.2024.110646","DOIUrl":"10.1016/j.ress.2024.110646","url":null,"abstract":"<div><div>Dynamic-reliability-based design optimization (DRBDO) is a promising methodology to address the significant challenge posed by the new generation of structural design theories centered around reliability considerations. Solving DRBDO problems typically requires iterations ranging from a dozen to several hundreds, with each iteration dedicated to updating the values of design variables. Furthermore, DRBDO necessitates hundreds of or even more representative structural analyses at each iteration to compute the reliability measure, which serves as a foundation for determining the search direction in the subsequent iteration. This results in the double-loop problem confronted by DRBDO, leading to substantial computational costs for structural re-computing, particularly in the cases involving complex nonlinear stochastic dynamical systems. In the present paper, a non-iterative DRBDO paradigms is proposed by combining a novel virtually conditional reliability evaluation and the newly proposed decoupled multi-probability density evolution method (M-PDEM). By leveraging the decoupled M-PDEM, a series of one-dimensional partial differential equations (PDEs) named Li-Chen equations are solved to calculate the joint PDF of multiple responses. This enables efficient computation of the joint probability density function (PDF) of design variables and extreme response as well as the conditional PDF of the extreme response given the values of design variables based on finite representative structural analyses. Then, the reliability of different designs can be regarded as the integral of the conditional PDF, which yields the reliability feasible domain. For problems that the objective function is monotonic to each design variables, by combining with a direct search technique, this method transforms the optimization process into true iteration-free calculations, and thereby eliminates the significant computational burden associated with structural re-computing at different intermediate designs in optimization iterations. Finally, the accuracy and effectiveness of this novel method are validated through numerical examples.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110646"},"PeriodicalIF":9.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142697252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling nuclear power plant piping reliability by coupling a human reliability analysis-based maintenance model with a physical degradation model","authors":"John Beal,&nbsp;Seyed Reihani,&nbsp;Tatsuya Sakurahara,&nbsp;Ernie Kee,&nbsp;Zahra Mohaghegh","doi":"10.1016/j.ress.2024.110655","DOIUrl":"10.1016/j.ress.2024.110655","url":null,"abstract":"<div><div>Reliability and availability analysis for repairable components, considering the underlying physical degradation and maintenance, is crucial in support of risk assessment and management. In nuclear power plants (NPPs), reactor coolant piping is a representative example of safety-critical repairable components that are subjected to long-term physical degradation interacting with maintenance activities. The existing methods for piping reliability analysis suffer from a limitation in their capability to analyze the time-dependent physics-maintenance interactions that could occur during the component lifetime and alter the underlying maintenance processes, for instance, an enhancement of maintenance programs based on condition monitoring data or an observed defect. To address this limitation, this paper develops a new piping reliability analysis methodology that couples a physics-of-failure (PoF) model with a maintenance performance analysis model. The contributions of this paper are two-fold: (i) developing a human reliability analysis (HRA)-based maintenance performance analysis model for NPP piping that can quantify maintenance outcomes under multiple types of maintenance programs, including time-based and condition-based preventive maintenance; and (ii) developing a computational methodology to couple the HRA-based maintenance performance analysis model with PoF models. The proposed physics-maintenance coupling methodology is applied to an NPP piping case study.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110655"},"PeriodicalIF":9.4,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine remaining useful life prediction method based on global-local attention compensation network","authors":"Zhixiang Chen","doi":"10.1016/j.ress.2024.110652","DOIUrl":"10.1016/j.ress.2024.110652","url":null,"abstract":"<div><div>Accurate remaining useful life (RUL) prediction is essential for ensuring the safe operation of machinery. The extraction of high-level features that contain both global dependencies and local refinements can effectively improve the accuracy of RUL predictions. In order to extract high-level features, this paper proposes a global-local attention compensation network (GLACN) for RUL prediction. The proposed network integrates a global interaction-feature (GIF) mechanism, a long short-term memory network (LSTM), and a local attention enhanced residual compensation (LAERC) mechanism. Initially, the GIF mechanism is used to processed selected signals from multiple sensors to facilitate global information interaction and allocate channel attention weights. Subsequently, the LSTM is employed to extract global temporal features and establish long-term dependencies among them. Finally, the global temporal features extracted by LSTM are further refined by LAERC to mine local features. To address the potential weakening of long-term dependencies during feature refinement, the global temporal features from the last hidden layer of LSTM are utilized as compensation, concatenated with refined features to generate final features. The effectiveness of the designed model for RUL prediction is tested by two benchmark datasets. The results illustrate that the prediction performance of the GLACN outperforms some of some state-of-the-art (SOTA) methods.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110652"},"PeriodicalIF":9.4,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pseudo-label assisted contrastive learning model for unsupervised open-set domain adaptation in fault diagnosis","authors":"Weicheng Wang ,&nbsp;Chao Li ,&nbsp;Zhipeng Zhang ,&nbsp;Jinglong Chen ,&nbsp;Shuilong He ,&nbsp;Yong Feng","doi":"10.1016/j.ress.2024.110650","DOIUrl":"10.1016/j.ress.2024.110650","url":null,"abstract":"<div><div>The operation of mechanical equipment is frequently characterized by complexity and variability, leading to signal domain shifts. This phenomenon underscores the significance of cross-domain fault diagnosis for maintaining the reliability and safety of mechanical systems. Due to the absence of labeled data in many operational contexts, there's a clear need for an unsupervised domain adaptation technique that does not rely on labeled information. Moreover, traditional domain adaptation methods presuppose identical label distributions across source and target domains. Nevertheless, real-world engineering scenarios often present novel fault categories out of distribution, thereby challenging the efficacy of established domain adaption methods. To address these challenges, we proposed a pseudo-label assisted contrastive learning model (PLA-CLM) for Unsupervised Open-set Domain Adaptation. Based on contrastive learning, the proposed model effectively minimizes the discrepancy between samples of identical pseudo-label across domains, while simultaneously integrating distance, density, and entropy to isolate out-of-distribution samples. After training, the model adaptively identifies known faults and detects OOD faults using thresholds calculated based on sample distribution. Experimental results on two datasets demonstrate that our method surpasses existing approaches, ensuring enhanced reliability of mechanical systems’ operation and maintenance.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110650"},"PeriodicalIF":9.4,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142697246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trustworthy Bayesian deep learning framework for uncertainty quantification and confidence calibration: Application in machinery fault diagnosis","authors":"Hao Li,&nbsp;Jinyang Jiao,&nbsp;Zongyang Liu,&nbsp;Jing Lin,&nbsp;Tian Zhang,&nbsp;Hanyang Liu","doi":"10.1016/j.ress.2024.110657","DOIUrl":"10.1016/j.ress.2024.110657","url":null,"abstract":"<div><div>Reliable and accurate machinery fault diagnosis is crucial for ensuring operational safety and reducing downtime in industrial settings. Traditional intelligent diagnosis methods only focus on improving the accuracy of in-distribution samples, but neglect the trustworthiness evaluation of diagnosis results. To address these issues, this paper developed a novel trustworthy machinery fault diagnosis (TMFD) method, which integrates Bayesian deep learning techniques with model calibration strategies. Specifically, TMFD regards a Bayesian convolutional neural network framework as the backbone. Then, we introduce α-divergence to facilitate the decomposition and quantification of epistemic uncertainty and aleatoric uncertainty, ultimately achieving out-of-distribution sample detection through epistemic uncertainty. Then, the ante-calibration loss constraint and the compositional post-calibration operation are jointly applied to promote data-efficient and high expressive calibration for in-distribution sample diagnosis confidence. Finally, TMFD is validated using three experimental datasets, demonstrating its effectiveness and robustness in machinery fault diagnosis.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110657"},"PeriodicalIF":9.4,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aerodynamic robustness optimization of aeroengine fan performance based on an interpretable dynamic machine learning method","authors":"Hongzhi CHENG ,&nbsp;Ziqing ZHANG ,&nbsp;Xingen LU ,&nbsp;Penghao DUAN ,&nbsp;Junqiang ZHU","doi":"10.1016/j.ress.2024.110654","DOIUrl":"10.1016/j.ress.2024.110654","url":null,"abstract":"<div><div>Aeroengines and gas turbines are susceptible to uncertainties during manufacturing and operation, leading to reduced efficiency and dispersed performance. Current engine design system often produces deterministic performance databases that cannot be effectively used to guide the uncertainty analysis and robust design process of turbomachinery. This paper proposes an interpretable dynamic machine learning method for sensitivity analysis and robust optimization of turbomachinery blades. A dynamic extreme gradient boosting (XGBoost) is trained to predict fan aerodynamic performance, and the SHapley additional explanation (SHAP) method is introduced to explain regression model behavior and identify the impact of uncertain variables. On this basis, the Lipschitz-based trust region (MAXLIPO-TR) optimization algorithm is used to obtain the optimal configuration with the best robustness performance. Finally, the method is applied to data mining for design guidelines of robustness performance enhancement of an aeroengine fan. The results show that maximum camber and tangential stacking have major effects on fan performance dispersion. The standard deviation of the isentropic efficiency, pressure ratio and mass flow rate of the optimized configuration are reduced by 42.4%, 35.6% and 22.7% respectively at design conditions. The proposed data mining method has scientific significance and industrial application value in the robust design of advanced turbomachinery.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110654"},"PeriodicalIF":9.4,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142697249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An uncertainty-incorporated active data diffusion learning framework for few-shot equipment RUL prediction","authors":"Chao Zhang ,&nbsp;Daqing Gong ,&nbsp;Gang Xue","doi":"10.1016/j.ress.2024.110632","DOIUrl":"10.1016/j.ress.2024.110632","url":null,"abstract":"<div><div>In predicting the remaining useful life (RUL) of critical equipment, the challenge of obtaining degradation data and the limitation of data volume lead to few-shot problems that significantly impact prediction accuracy. To address this issue, this paper introduces a reinforcement learning feedback loop mechanism for predicting the RUL of critical equipment. Initially, the framework uses a data diffusion model to generate a dataset that closely approximates the distribution of the labeled samples for data augmentation. Subsequently, Bayesian deep learning and Monte Carlo (MC) dropout inference provide uncertainty quantifications for RUL interval predictions. An active learning strategy, which is based on uncertainty and diversity, converts unlabeled samples into labeled samples, thereby selecting the most effective training dataset. In each iteration, the model adjusts its strategy for selecting and generating data based on the current state of learning, dynamically adapting to the needs of the learning process via Bayesian methods. The proposed prediction framework was validated through experiments using the C-MAPSS and NASA battery datasets. The results indicate that the application of data diffusion and active learning strategies significantly enhances prediction performance, increasing confidence by 42 %. Comparative experiments with other benchmark methods demonstrate that the proposed method reduces prediction uncertainty by at least 15 %.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110632"},"PeriodicalIF":9.4,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142697247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A three-stage bearing transfer fault diagnosis method for large domain shift scenarios","authors":"Kai Huang ,&nbsp;Zhijun Ren ,&nbsp;Linbo Zhu ,&nbsp;Tantao Lin ,&nbsp;Yongsheng Zhu ,&nbsp;Li Zeng ,&nbsp;Jin Wan","doi":"10.1016/j.ress.2024.110641","DOIUrl":"10.1016/j.ress.2024.110641","url":null,"abstract":"<div><div>In recent years, significant progress has been achieved in the intelligent fault diagnosis of bearings based on transfer learning. However, existing methods overlook the presence of domain-specific features that are non-transferable when aligning domain distributions. Additionally, the reliability of subdomain alignment has not been adequately evaluated. This severely restricts the diagnostic performance of transfer learning, especially in scenarios of large domain shifts. To address these issues, this paper proposes a novel approach based on three-stage transfer alignment. In the first stage, two private encoders, and a shared encoder are designed to eliminate domain-specific features, thus maximizing the effectiveness and transferability of shared encoded features. Subsequently, in the second stage, a deep adversarial domain adaptation method is introduced to adapt the global distributions between the two domains. Lastly, the third stage presents a novel soft pseudo-label distillation method, based on adaptive entropy weighting. This enhances alignment between subdomains, further bridging the distribution gap between the two domains. A series of comprehensive experiments under two types of large domain shift scenarios validate that the proposed method has a superior performance and could boost 6.93 % and 6.14 % accuracy than the state-of-the-art methods, respectively.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110641"},"PeriodicalIF":9.4,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distance similarity entropy: A sensitive nonlinear feature extraction method for rolling bearing fault diagnosis","authors":"Tao Wang ,&nbsp;Shin Yee Khoo ,&nbsp;Zhi Chao Ong ,&nbsp;Pei Yi Siow ,&nbsp;Teng Wang","doi":"10.1016/j.ress.2024.110643","DOIUrl":"10.1016/j.ress.2024.110643","url":null,"abstract":"<div><div>Entropy-based methods are widely used in machinery fault diagnosis for characterizing system disorder and complexity. However, conventional entropy techniques often fail to capture local signal variations when analyzing relationships between vectors, especially in complex settings. This leads to incomplete representations of subtle features and dynamic behaviors, resulting in inaccurate estimations of system complexity and affecting diagnostic accuracy and reliability. To address these limitations, a novel distance similarity entropy (DSEn) is proposed in this paper: (1) It leverages element-wise distance to precisely capture local shifts and subtle distortions between subsequences. (2) It employs a Gaussian kernel function for vector similarity, enhancing signal pattern analysis by preserving subtle differences and mitigating the impact of outliers. (3) It uses probability density estimation of distance similarities between adjacent vectors to track changes in internal signal patterns, enabling more accurate and sensitive estimations of signal complexity. Synthetic signal experiments demonstrate that DSEn excels in detecting dynamic time series changes and characterizing signal complexity. Tests on two bearing datasets reveal that DSEn's extracted features show significant differences, highlighted by Hedges’ <em>g</em> effect size. Compared to other commonly used entropies (SampEn, PermEn, FuzzEn, DistEn, etc.), DSEn shows superior fault identification accuracy, computational efficiency, and noise resistance.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110643"},"PeriodicalIF":9.4,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}