Reliability Engineering & System Safety最新文献

{"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":null,"pages":null},"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":null,"pages":null},"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":"Sinkhorn divergence-based contrast domain adaptation for remaining useful life prediction of rolling bearings under multiple operating conditions","authors":"Yan Han ,&nbsp;Ailin Hu ,&nbsp;Qingqing Huang ,&nbsp;Yan Zhang ,&nbsp;Zhichao Lin ,&nbsp;Jinghua Ma","doi":"10.1016/j.ress.2024.110557","DOIUrl":"10.1016/j.ress.2024.110557","url":null,"abstract":"<div><div>Under multiple operating conditions, the degradation characteristics of rolling bearings show diverse distributions. Domain adaptation (DA) achieves effective alignment between source and target domains by extracting domain-invariant features. However, in the prediction of remaining useful life (RUL) for bearings, numerous DA methods overlook mutual information from target-specific data and encounter potential challenges such as the vanishing gradient problem during the alignment of data distributions, leading to limited performance. To address these challenges, a novel method called Sinkhorn Divergence-based Contrast Domain Adaptation (SD_CDA) is proposed to predict RUL under multiple operating conditions. Firstly, an adversarial training framework is constructed to initially extract domain-invariant features. Subsequently, the cross-domain temporal mixup strategy is proposed for the data augment, which obtains positive samples to serve contrastive learning. Then self-supervised momentum contrast (MoCo) is employed to extract mutual information from target-specific data, preserving its specificity. Finally, Sinkhorn divergence is introduced to further align the fine-grained structure of the source domain and target domain, and enhance the transfer ability of the model. The experimental results demonstrate the superiority and effectiveness of the proposed method under multiple operating conditions.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420989","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":null,"pages":null},"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":"Assessment of corrosion probability of steel in mortars using machine learning","authors":"Haodong Ji,&nbsp;Yuhui Lyu,&nbsp;Zushi Tian,&nbsp;Hailong Ye","doi":"10.1016/j.ress.2024.110535","DOIUrl":"10.1016/j.ress.2024.110535","url":null,"abstract":"<div><div>Corrosion assessment enables engineers to quickly discern the corrosion status of steel in concrete structures. However, existing assessment methods mainly rely on a single-factor and exhibit poor adaptability to various corrosion scenarios. Moreover, most methods are traditional deterministic approach, which ignores the uncertainties in corrosion assessments. In this work, machine learning (ML) is employed to develop a multifactor classification model for multi-level corrosion status assessment, together with corresponding corrosion probability maps. First, a comprehensive corrosion dataset was collected, including relative humidity (RH), electrical resistivity (ER), corrosion potential (CP), and corrosion rate (CR). The CR was used to subdivide different corrosion levels, and ML classification models were established for three-factor and two-factor scenarios. The optimal model was then used to create corrosion probability maps for various corrosion levels. The results indicated that the poor reliability and accuracies in current corrosion assessment methods originated from the inconsistent corrosion behaviors induced by carbonation and chloride in concrete. Moreover, when using the corrosion probability maps to assess corrosion status of steel in mortars, CP and ER should first be used to determine if the steel is in an active state, followed by RH and CP to evaluate whether it is in a severe-corrosion state.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421440","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":null,"pages":null},"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}

{"title":"Wind turbine fault detection and identification via self-attention-based dynamic graph representation learning and variable-level normalizing flow","authors":"Yunyi Zhu ,&nbsp;Bin Xie ,&nbsp;Anqi Wang ,&nbsp;Zheng Qian","doi":"10.1016/j.ress.2024.110554","DOIUrl":"10.1016/j.ress.2024.110554","url":null,"abstract":"<div><div>Effective wind turbine (WT) condition monitoring is significant to improve wind power generation efficiency and reduce operation and maintenance costs. Supervisory control and data acquisition (SCADA) data are widely utilized for WT condition monitoring due to their low cost and accessibility. However, the intricate interdependencies among SCADA variables affect the accuracy of WT fault detection, and few methods provide identification for the anomaly cause. To solve these issues, this paper proposes an unsupervised fault detection and identification method based on self-attention-based dynamic graph representation learning and variable-level normalizing flow. Firstly, a dynamic graph representation learning model based on spatial and temporal self-attention mechanisms is proposed. It can effectively learn the dynamic and mutual relations among variables for early fault detection. Secondly, a variable-level normalizing flow is proposed for discriminative density estimation of variables, which can realize component fault localization. Finally, a node deviation index based on contrast graph is proposed to identify the root cause of anomalies. Experimental results using WT data from a wind farm in Northwest China prove that the proposed method has better accuracy and interpretability in WT fault detection and identification, which displays better effectiveness in practical wind energy applications.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420985","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":"Probabilistic risk assessment of civil aircraft associated failures under condition-based maintenance","authors":"Yuanyuan Guo ,&nbsp;Youchao Sun ,&nbsp;Qingmin Si ,&nbsp;Xinyao Guo ,&nbsp;Nongtian Chen","doi":"10.1016/j.ress.2024.110550","DOIUrl":"10.1016/j.ress.2024.110550","url":null,"abstract":"<div><div>Maintenance can improve an aircraft system's reliability over a long operation period or when a component has failed. However, inappropriate maintenance inspection intervals will cause latent failures to be covered or undetected, leading to a large number of unplanned flight disruptions for airlines. In this paper, we present a two-stage framework to assess the associated failure risk of civil aircraft under condition-based maintenance. In the first stage of the framework, the probability of primary functional failure across the lifecycles of the monitored component is determined by analyzing whether the current inspection interval prevents the component from progressing from latent failure to functional failure. In the second stage of the framework, the associated failure probability between components and related systems is formulated by the adjacency matrix. The structure and performance of the proposed model were tested on a case study by run-to-failure data associated with aircraft engines from a large airline. Focusing on the scenario of turbine disk cracking leading to fragment penetration of the fuel tank and causing fire as the consequential fault impact path, the results show that the risk of aircraft fire caused by turbine disk fragments falls within an acceptable range, necessitating the completion of inspections and subsequent monitoring within the stipulated timeframe. The method can be used to readjust the inspection interval, optimize the operation plan, improve the on-time performance of flights, and reduce the risk of aviation accidents.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421436","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":"Improving port state control through a transfer learning-enhanced XGBoost model","authors":"Ruihan Wang ,&nbsp;Mingyang Zhang ,&nbsp;Fuzhong Gong ,&nbsp;Shaohan Wang ,&nbsp;Ran Yan","doi":"10.1016/j.ress.2024.110558","DOIUrl":"10.1016/j.ress.2024.110558","url":null,"abstract":"<div><div>With the advancements in modern information technology, Port State Control (PSC) inspections, as a crucial measure to protect ship safety and the marine environment, are undergoing an intelligent transformation. This paper aims to streamline the selection process for inspecting high-risk ships by employing a data-driven model to predict the number of deficiencies in ships arriving at ports. A transfer learning-enhanced eXtreme Gradient Boosting (XGBoost) model is proposed by innovatively incorporating sample similarity calculations to adapt the model to the unique characteristics of the target port. This novel model enables the integration of relevant data from other ports, enhancing the predictive performance of the model to specific port conditions. Utilizing PSC inspection records from ports within the Tokyo Memorandum of Understanding (MoU) and choosing the port of Singapore as the target, numerical experiments demonstrate that the proposed model achieves improvements of 1.81 %, 6.08 %, and 3.60 % in the mean absolute error, mean squared error and root mean squared error, respectively, compared to the standard XGBoost model. Furthermore, across various sizes of training samples, the proposed model outperforms other machine learning models. This work may service as a significant step towards exploring the potential of developing data-driven models based on comprehensive data to assess the risk level of foreign ships arriving at ports, ameliorating the PSC inspection process by aiding PSC officers in identifying substandard ships more effectively.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421569","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":"Prediction model optimization of gas turbine remaining useful life based on transfer learning and simultaneous distillation pruning algorithm","authors":"Yu Zheng,&nbsp;Liang Chen,&nbsp;Xiangyu Bao,&nbsp;Fei Zhao,&nbsp;Jingshu Zhong,&nbsp;Chenhan Wang","doi":"10.1016/j.ress.2024.110562","DOIUrl":"10.1016/j.ress.2024.110562","url":null,"abstract":"<div><div>For the application of deep learning (DL) models in the field of remaining useful life (RUL) prediction and predictive maintenance (PdM) of complex equipment, the insufficient training data and large model are the two major problems. To address these issues, a model training method based on transfer learning and a simultaneous distillation pruning algorithm were proposed. By introducing prior knowledge, three transfer learning modes are devised to reduce the demand of training data. Additionally, the simultaneous distillation pruning algorithm was devised to make the model lightweight, and an iterative pruning method was adopted to trim the large neural network model. By analyzing the performance of different transfer learning modes, the effectiveness of the proposed method can be demonstrated. The number of model parameters and the performance before and after pruning were compared. The results demonstrated that, without significant alterations to the prediction performance, the proposed model exhibited the capability to markedly reduce the number of model parameters. Based on the proposed methods, the challenges of insufficient data and efficiency encountered by DL models could be effectively addressed.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420995","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}