Reliability Engineering & System Safety最新文献

{"title":"A closed-form continuous-depth neural-based hybrid difference features re-representation network for RUL prediction","authors":"Xuanlin Li ,&nbsp;Yawei Hu ,&nbsp;Hang Wang ,&nbsp;Yongbin Liu ,&nbsp;Xianzeng Liu ,&nbsp;Huitian Lu","doi":"10.1016/j.ress.2024.110540","DOIUrl":"10.1016/j.ress.2024.110540","url":null,"abstract":"<div><div>Remaining Useful Life (RUL) prediction contributes to ensuring the reliability of mechanical systems and improving their maintenance plans. Recently, prediction methods based on deep learning have undergone rapid development. However, there are significant differences between multivariate monitoring sequences and utilizing a single model for feature extraction, which leads to reaching suboptimization. Additionally, focusing solely on a specific feature dimension usually imposes notable limitations on the model. This paper proposes a Closed-form Continuous-depth neural (CfC)-based hybrid difference Features Re-representation Network (CfC-F2RN) for RUL prediction. This method comprehensively utilizes hybrid difference features through two stages: initial feature representation and feature re-representation. In the initial feature representation stage, a Long Short-Term Memory (LSTM) is employed to capture the hidden state information of each time step in sequence data. Next, a novel attention mechanism is utilized to extract the hidden states and obtain a deep feature map. In the feature re-representation stage, the encoded features are re-represented using a decoder composed of the CfC model. Finally, the hidden state of the last LSTM unit in the encoder, serving as supplementary information, is combined with the decoded features to form a dual-latent feature representation of the mixed differential features. The prediction subnetwork is then adopted to accomplish RUL prediction. The superiority of CfC-F2RN is substantiated through benchmarking against established methods using the C-MAPSS dataset.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"253 ","pages":"Article 110540"},"PeriodicalIF":9.4,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421570","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 Novel Reinforcement Learning Agent for Rotating Machinery Fault Diagnosis with Data Augmentation","authors":"Zhenning Li,&nbsp;Hongkai Jiang,&nbsp;Xin Wang","doi":"10.1016/j.ress.2024.110570","DOIUrl":"10.1016/j.ress.2024.110570","url":null,"abstract":"<div><div>The problem of data imbalance and limited sample poses a prominent challenge to fault diagnosis in industry, especially in rotating machinery fault diagnosis due to the constraints of sampling and labeling. Reinforcement learning is known for its auto-optimized learning ability but it is severely limited by the quality and quantity of data it is able to collect. This paper applies the Equilibrium Deep Q-Network (Equilibrium-DQN) based agent with the Variational Autoencoder with Wasserstein Generative Adversarial Network and Gradient Penalty (VAE-WGAN-GP) for rotating machinery fault diagnosis. The Equilibrium-DQN possesses the property of leveraging multiple target networks to improve training stability and accuracy. The VAE-WGAN-GP excels in resolving data scarcity problems in fault diagnosis, especially in enriching labeled data with more typical samples while keeping the original data features. Herein, a novel framework is presented that integrates both data augmentation techniques with reinforcement learning agent for fault diagnosis. The proposed framework improves the weakness of insufficient samples in fault diagnosis and reveals its potential in complex industrial applications. The experiments, both on the test bench dataset and on the real locomotive dataset, confirm the advantage of the proposed framework.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"253 ","pages":"Article 110570"},"PeriodicalIF":9.4,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420981","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":"Exploring key factors for long-term vessel incident risk prediction","authors":"Tianyi Chen ,&nbsp;Hua Wang ,&nbsp;Yutong Cai ,&nbsp;Maohan Liang ,&nbsp;Qiang Meng","doi":"10.1016/j.ress.2024.110565","DOIUrl":"10.1016/j.ress.2024.110565","url":null,"abstract":"<div><div>Factor analysis acts a pivotal role in enhancing maritime safety. Most previous studies conduct factor analysis within the framework of incident-related label prediction, where the developed models can be categorized into short-term and long-term prediction models. The long-term models offer a more strategic approach, enabling more proactive risk management, compared to the short-term ones. Nevertheless, few studies have devoted to rigorously identifying the key factors for the long-term prediction and undertaking comprehensive factor analysis. Hence, this study aims to delve into the key factors for predicting the incident risk levels in the subsequent year given a specific datestamp. The majority of candidate factors potentially contributing to the incident risk are collected from vessels’ historical safety performance data spanning up to five years. An improved embedded feature selection method, which integrates Random Forest classifier with a feature filtering process, is proposed to identify key risk-contributing factors from the candidate pool. A dataset with information of 131,652 vessels collected from 2015 to 2023 is utilized for case study. The results demonstrate superior performances of the proposed method in incident prediction and factor interpretability. Comprehensive analysis is conducted upon the key factors, which could help maritime stakeholders formulate management strategies for incident prevention.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"253 ","pages":"Article 110565"},"PeriodicalIF":9.4,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420983","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":"Letting losses be lessons: Human-machine cooperation in maritime transport","authors":"Shiqi Fan ,&nbsp;Kun Shi ,&nbsp;Jinxian Weng ,&nbsp;Zaili Yang","doi":"10.1016/j.ress.2024.110547","DOIUrl":"10.1016/j.ress.2024.110547","url":null,"abstract":"<div><div>Navigation safety has been a critical guarantee of global shipping, and it becomes more challenging given the increasing employment of advanced technologies and novel ship design in the era of Maritime Autonomous Surface Ships (MASS). The human-centred risk analysis of human-machine cooperation is scarce in general and emerging in maritime transport in specific. This paper aims to develop a new approach enabling the analysis of significant risk influencing factors (RIFs) in human-machine cooperation through an in-depth investigation of the occurred mistakes and violations in the cooperative operations of seafarers and machines in maritime transport. Its novelties consist of (1) a novel approach to analysing and quantifying the connectivity between humans and machines in safety-critical operations, (2) new integration of the frequency and impact of RIFs in the human-machine cooperation model, and (3) ultilisation of graph theory to generate a network to analyse critical human-machine RIFs and their interactions with the system. The connectivity analysis of RIFs is conducted through a weighted undirected network, showing the features of RIF connectivity accommodating the closed-loop system. The proposed novel approach, which combines the frequency and impact features to identify critical RIFs and analyses graphical features, will aid to realise the human-centred risk analysis for MASS. The findings make contributions for ship designers to rationalise the clustering design of function-based automation and training organisations to improve seafarer skills by rationally considering the identified risk-based human-machine cooperation features, and providing new competence schemes that can fit the demands of MASS in future.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"253 ","pages":"Article 110547"},"PeriodicalIF":9.4,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DNN-metamodeling and fragility estimate of high-rise buildings with outrigger systems subject to seismic loads","authors":"Lili Xing ,&nbsp;Paolo Gardoni ,&nbsp;Ying Zhou ,&nbsp;Peng Zhang","doi":"10.1016/j.ress.2024.110572","DOIUrl":"10.1016/j.ress.2024.110572","url":null,"abstract":"<div><div>This paper proposed deep neural networks (DNNs) for the dynamic response of high-rise buildings with one-outrigger systems under two types of seismic hazards. Using an existing database, the hyperparameters for the architecture of the DNNs are determined finding a trade-off between accuracy and complexity. The performance of the proposed DNNs is compared with two metamodels in the literature, a probabilistic demand model and a kriging metamodel. Partial dependence plot and SHapley Additive exPlanations are used for the explanation of the marginal effect of each feature on the predicted outcome of a neural network from the perspective of global and local agnostic. Considering the uncertainty in the input features, the DNNs are then used to formulate fragility estimates for example high-rise buildings with three types of outrigger systems. The model-agnostic analysis suggests that the DNN targeting the inter-story drift and top acceleration shows extremely high sensitivity to variation in the seismic hazard features, earthquake magnitude, and rupture distance. The fragility curves objectively quantify the reliability of buildings with each of the three outrigger systems and show the effectiveness of damped outrigger systems in reducing fragilities.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"253 ","pages":"Article 110572"},"PeriodicalIF":9.4,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421432","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":"Physics-based digital twin updating and twin-based explainable crack identification of mechanical lap joint","authors":"Wongon Kim ,&nbsp;Byeng D. Youn","doi":"10.1016/j.ress.2024.110515","DOIUrl":"10.1016/j.ress.2024.110515","url":null,"abstract":"<div><div>The mechanical joints, including the lap joint, weld, bolt, and pin, are vulnerable to fatigue failure because of stress concentration and internal flaws. Digital twin (DTw) strategies were proposed to prevent catastrophic system failure by fatigue damage in mechanical joints. In previous studies, the data-driven approach, such as deep learning and machine learning were utilized to estimate severity of the damage. However, it needs to improve its prediction accuracy because of insufficient data and physical interpretability. In this study, the physics-based digital twin model updating and twin-based crack identification of fatigue damage in riveted lap joints were proposed using lamb waves with consideration of uncertain crack growth path. The proposed approach is based on three techniques; (i) Data pre-processing, including filtering and optimization-based signal synchronization, (ii) Lamb-wave propagation analysis with sensor dynamics model and uncertain crack path, and (iii) Optimization based physics-based model updating and inference. In data pre-processing, the excitation frequency magnitude and truncation time are estimated using the observed actuator signal in the Lamb-wave test. The sensor dynamic model and model parameters are updated using the Bayesian optimization method to minimize both the errors in the predicted (<span><math><msub><mover><mi>y</mi><mo>^</mo></mover><mi>t</mi></msub></math></span>) and observed (<span><math><msub><mi>y</mi><mi>t</mi></msub></math></span>) wave signal and the errors in the inferred (<span><math><msup><mrow><mi>l</mi></mrow><mo>*</mo></msup></math></span>) and observed (<span><math><mi>l</mi></math></span>) crack length. The crack growth path is sampled based on angular and spline schemes to consider uncertain crack propagation paths. The validity of the proposed method is demonstrated using an open data set (2019 PHM society data challenge). The results conclude that the proposed digital twin approach can improve estimation accuracy considering both the crack growth path and sensor dynamics model.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"253 ","pages":"Article 110515"},"PeriodicalIF":9.4,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421433","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 new active learning surrogate model for time- and space-dependent system reliability analysis","authors":"Hongyou Zhan,&nbsp;Ning-Cong Xiao","doi":"10.1016/j.ress.2024.110536","DOIUrl":"10.1016/j.ress.2024.110536","url":null,"abstract":"<div><div>This study introduces a novel method for time- and space-dependent system reliability analysis, integrating an active learning surrogate model with an innovative parallel updating strategy. A global Kriging model is developed to represent the signs of random samples using efficient global optimization. From a Bayesian perspective, the prediction probabilities of random sample signs within the time-space domain are calculated, and the sample with the lowest prediction probability is chosen to update the global Kriging model. The system extremum for each sample in the time-space domain is determined, and the corresponding random variables, time-space coordinates, and failure modes are selected. To further decrease iteration times, a parallel updating strategy that considers both the predicted probability and the correlation among candidate samples is proposed. Additionally, a new stopping criterion is introduced to balance accuracy and efficiency, terminating the updating process appropriately. The method's accuracy and efficiency are validated through three examples.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"253 ","pages":"Article 110536"},"PeriodicalIF":9.4,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421571","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":"Reliability analysis for running safety of vehicle on slab track via an improved second-order fourth-moment approach","authors":"Ping Lou ,&nbsp;Tao Shi ,&nbsp;T.Y. Yang","doi":"10.1016/j.ress.2024.110555","DOIUrl":"10.1016/j.ress.2024.110555","url":null,"abstract":"<div><div>The reliability assessment for running safety of vehicle on slab track under temperature action and seismic excitation is challenging due to the extensive computational efforts. This study proposes an improved second-order fourth-moment (ISOFM) approach for the reliability analysis for running safety of vehicle on slab track under temperature action and seismic excitation. The dimensionless limit state function (LSF) for running safety of vehicle on slab track is formulated considering four failure modes. The proposed ISOFM approach is verified by the Monte Carlo simulation (MCS) method. The proposed ISOFM approach greatly reduces the number of computations of nonlinear LSF. The merit of the proposed ISOFM approach lies in significantly enhancing the computation efficiency and precision of reliability considering nonlinear LSF with multi-failure modes. Additionally, the influences of temperature action and seismic excitation on the reliability for running safety of vehicle on slab track are discussed. The reliability for vehicle running safety decreases more significantly with the increment in overall temperature compared to the positive temperature gradient (PTG). The reliability for running safety of vehicle on slab track decreases nonlinearly with the increase of the earthquake intensity.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"253 ","pages":"Article 110555"},"PeriodicalIF":9.4,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433322","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 bearing fault data augmentation method based on hybrid-diversity loss diffusion model and parameter transfer","authors":"Yuan Wei ,&nbsp;Zhijun Xiao ,&nbsp;Xiangyan Chen ,&nbsp;Xiaohui Gu ,&nbsp;Kai-Uwe Schröder","doi":"10.1016/j.ress.2024.110567","DOIUrl":"10.1016/j.ress.2024.110567","url":null,"abstract":"<div><div>The fault diagnosis of mechanical equipment can prevent potential mechanical failures, avoid property damage and personal injury, and ensure the stable and safe operation of mechanical equipment. Data driven is an important aspect of intelligent fault diagnosis. When data is scarce, it can seriously affect the accuracy of fault diagnosis and make it difficult to ensure the smooth and safe operation of machinery. Faced with this challenge, a classifier-free guidance diffusion model combining hybrid loss and diversity loss (CFGDMHD) is proposed for data augmentation of fault samples. This new data augmentation method generates samples with the same data distribution as real samples from random noise through diffusion process. CFGDMHD can generate multi-class samples simultaneously without the need for additional classifier guidance in the joint training of unconditional diffusion models and conditional diffusion models. This work proposes diversity loss to improve the diversity of generated samples. We conducted experiments using a bearing dataset. The results indicate that the sample quality and diversity generated by this method are excellent, which can help improve the accuracy of fault diagnosis and ensure the safe operation of mechanical systems.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"253 ","pages":"Article 110567"},"PeriodicalIF":9.4,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433325","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":"SRSGCN: A novel multi-sensor fault diagnosis method for hydraulic axial piston pump with limited data","authors":"Pengfei Liang ,&nbsp;Xiangfeng Wang ,&nbsp;Chao Ai ,&nbsp;Dongming Hou ,&nbsp;Siyuan Liu","doi":"10.1016/j.ress.2024.110563","DOIUrl":"10.1016/j.ress.2024.110563","url":null,"abstract":"<div><div>Deep learning has immense potential in ensuring the safe operation of hydraulic axial piston pumps (HAPP). However, the complex operating environment and high cost of labeling have resulted in a scarcity of labeled fault samples. This paper proposes a novel method called Siamese Random Spatiotemporal Graph Convolutional Network (SRSGCN). Firstly, based on graph convolutional networks, a Random Spatiotemporal Graph (RSG) is designed to aggregate multi-sensor information at different time stamps, fully exploiting the spatiotemporal features of the original data. Secondly, the Siamese Neural Network (SNN) is improved by retaining the twin subnetwork structure and removing the similarity output part. While preserving feature extraction capabilities, it is endowed with classification ability. Based on its strong feature mining capability, SRSGCN can fully utilize the scarce sample information to improve diagnostic accuracy. Finally, a case study was conducted on our HAPP experimental platform. The experiments show that compared with other existing methods, this method has higher diagnostic accuracy and can effectively solve the problem of HAPP fault diagnosis under limited data conditions.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"253 ","pages":"Article 110563"},"PeriodicalIF":9.4,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420984","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}