2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS)最新文献_第5页

Research on Feature Selection Method Based on Bayesian Network and Importance Measures 基于贝叶斯网络和重要性度量的特征选择方法研究

2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS) Pub Date : 2022-08-21 DOI: 10.1109/ICRMS55680.2022.9944576

Jingwei Zhang, Chen Chen, Yuhan Wu, Shubin Si, Z. Geng, Zhiqiang Cai

引用次数: 0

Sensitivity Analysis-Based Multi-Modal Transportation Network Vulnerability Assessment with Weibit Choice Models 基于灵敏度分析的多式联运网络脆弱性评价与Weibit选择模型

2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS) Pub Date : 2022-08-21 DOI: 10.1109/ICRMS55680.2022.9944606

Y. Gu, A. Chen, G. Li

{"title":"Sensitivity Analysis-Based Multi-Modal Transportation Network Vulnerability Assessment with Weibit Choice Models","authors":"Y. Gu, A. Chen, G. Li","doi":"10.1109/ICRMS55680.2022.9944606","DOIUrl":"https://doi.org/10.1109/ICRMS55680.2022.9944606","url":null,"abstract":"This study proposes a sensitivity analysis-based multi-modal network vulnerability analysis through the variation in utility-based accessibility measures associated with mode and route choice dimensions. The weibit expected travel disutility is used to assess network accessibility, which is the composite travel cost derived from the weibit travel choice model. Benefiting from the properties of weibit model, the proposed accessibility measures can accurately reflect proportional variation in network performance and account for heterogenous network scales. The equilibrium mode and route choice patterns are reproduced via a weibit-based combined modal split and traffic assignment (CMSTA) model while specifically considering the mode similarity and route overlapping in travel choice modeling. The sensitivity analysis of the weibit-based CMSTA model with respect to inputs from both sides of supply and demand is conducted for vulnerability assessment and critical link identification. The sensitivity analysis-based method can reduce the computational burden of repetitively solving the CMSTA model required by commonly used enumeration- or scenario-based approaches. The proposed vulnerability measures and analysis method are demonstrated via numerical experiments on a multi-modal transportation network.","PeriodicalId":421500,"journal":{"name":"2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121598166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Maintenance Decision System Design Based on Equipment Support Data System 基于装备保障数据系统的维修决策系统设计

2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS) Pub Date : 2022-08-21 DOI: 10.1109/ICRMS55680.2022.9944557

Chenning Liu, Xin Kong, Hao Li, Siyuan Chen, Chang Zhou

引用次数: 0

A New Method for Deriving Reliability Qualification Test Plans 可靠性鉴定试验计划编制的新方法

2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS) Pub Date : 2022-08-21 DOI: 10.1109/ICRMS55680.2022.9944586

P. Jiang, Bo Wang, Dian Zhang, Jianjun Qi

引用次数: 0

A Remaining Useful Life Prediction Framework for Aero-engine Using Information Entropy-based Criterion and PCA-RVM 基于信息熵准则和PCA-RVM的航空发动机剩余使用寿命预测框架

2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS) Pub Date : 2022-08-21 DOI: 10.1109/ICRMS55680.2022.9944571

X. Li, L. Yang, X. Liu, F. Zhu

引用次数: 1

A Knowledge Graph-based Link Prediction for Interpretable Maintenance Planning in Complex Equipment 基于知识图的复杂设备可解释性维修计划环节预测

2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS) Pub Date : 2022-08-21 DOI: 10.1109/ICRMS55680.2022.9944561

Liqiao Xia, Pai Zheng, Shufei Li, Pin Lyu, C.K.M. Lee, Jialiang Zhou, K. Wang

{"title":"A Knowledge Graph-based Link Prediction for Interpretable Maintenance Planning in Complex Equipment","authors":"Liqiao Xia, Pai Zheng, Shufei Li, Pin Lyu, C.K.M. Lee, Jialiang Zhou, K. Wang","doi":"10.1109/ICRMS55680.2022.9944561","DOIUrl":"https://doi.org/10.1109/ICRMS55680.2022.9944561","url":null,"abstract":"Maintenance planning is a significant part of predictive maintenance, which involves task planning, resource scheduling, and prevention. Many data points will be collected during the monitoring and maintenance of sophisticated equipment thanks to the large-scale sensor systems installed in contemporary factories. As a result, with the help of collected maintenance data, maintenance plans may be more detailed and timelier. A knowledge graph (KG) has recently been proposed to manage massive and unorganized maintenance data semantically, enhancing data usage. Despite the fact that previous research had utilized KG for maintenance planning, they had only used semantic searching or graph structure-based algorithms and had not included the prediction of new links. To fill this gap, a maintenance-oriented KG is established firstly based on the well-defined ontology schema and accumulated maintenance data. Then, an Attention-Based Compressed Relational Graph Convolutional Network is proposed to find the potential solutions and explain the fault, specifically for the heterogeneous and sparse graph structure of maintenance-orient KG. A maintenance case of oil drilling equipment is carried out, which compares the proposed model with other cutting-edge models to demonstrate its effectiveness in link prediction.","PeriodicalId":421500,"journal":{"name":"2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128929802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

AtFP: Attention-based Failure Predictor for Extreme-scale Computing 极端规模计算的基于注意的故障预测器

2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS) Pub Date : 2022-08-21 DOI: 10.1109/ICRMS55680.2022.9944604

Longhao Li, T. Znati

{"title":"AtFP: Attention-based Failure Predictor for Extreme-scale Computing","authors":"Longhao Li, T. Znati","doi":"10.1109/ICRMS55680.2022.9944604","DOIUrl":"https://doi.org/10.1109/ICRMS55680.2022.9944604","url":null,"abstract":"Extreme-scale computing is paving the way for unparalleled advances in scientific discovery and innovation. However, as systems scale, their propensity to failure increases significantly, making it difficult for long running applications that span a large number of computing nodes to make forward progress. Achieving high performance in extreme scale environments, while minimizing energy consumption, has emerged as a daunting challenge. Significant advances on how to deal with failure, both physical and logical, have been achieved, with varying degree of success. A key component of fault tolerance relies heavily on the ability of the scheme to predict failure accurately. Varies approaches, including intelligent methods, have been proposed to predict failures. In this paper, we propose an attention-based failure predictor (AtFP), which automatically extracts representative features from the raw event log data to predict failure. The results show that, using the same input and output layers, AtFP outperforms frequently used LSTM methods. The proposed model reduces the F1 score by 39% and the training time by 65%.","PeriodicalId":421500,"journal":{"name":"2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS)","volume":"329 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122743233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Degradation Mechanism and Failure Analysis of Planar Type SiC MOSFETs Under Cyclic Stress of Surge Current 浪涌电流循环应力下平面型SiC mosfet退化机理及失效分析

2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS) Pub Date : 2022-08-21 DOI: 10.1109/ICRMS55680.2022.9944567

Yih-Jiun Lin, Y. Chen, K. Geng, Bo Hou

{"title":"Degradation Mechanism and Failure Analysis of Planar Type SiC MOSFETs Under Cyclic Stress of Surge Current","authors":"Yih-Jiun Lin, Y. Chen, K. Geng, Bo Hou","doi":"10.1109/ICRMS55680.2022.9944567","DOIUrl":"https://doi.org/10.1109/ICRMS55680.2022.9944567","url":null,"abstract":"The degradation behavior of planar silicon carbide metal oxide semiconductor field effect transistors (SiC MOSFETs) in terms of electrical characteristics is explored in this work under cyclic stress of surge current.Before and after surge current stress, planar-type SiC MOSFET is subjected to scanning electron microscope (SEM)-based failure analysis. During the surge current cyclic stress investigations, the accumulation of oxide trap charge and interface trap density leads to a drop in the threshold voltage(Vth) under surge current stress, according to the experimental data. The on-resistance(Rds(on)) displays the opposite tendency to the Vth during surge current cycling. The fact that it is a degradation phenomenon induced by the combined effect of Vth and package degradation caused by repetitive cycle thermal stress of the device is confirmed by testing. According to SEM studies, the major reason of SiC MOSFET failure following surge current cycling stress is that the surge current causes the Aluminum (Al) at the source terminal to melt and subsequently enter into the gate terminal, resulting in a gate-source short circuit and device failure.","PeriodicalId":421500,"journal":{"name":"2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS)","volume":"368 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122775422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reliability Analysis for Mixture Weibull Distribution with Progressively Censored Data Based on Stochastic Expectation-maximization Method 基于随机期望最大化法的渐进式截尾混合威布尔分布可靠性分析

2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS) Pub Date : 2022-08-21 DOI: 10.1109/ICRMS55680.2022.9944579

Y. T. Wu, H. Song, J. Zhou, Z. Lu

{"title":"Reliability Analysis for Mixture Weibull Distribution with Progressively Censored Data Based on Stochastic Expectation-maximization Method","authors":"Y. T. Wu, H. Song, J. Zhou, Z. Lu","doi":"10.1109/ICRMS55680.2022.9944579","DOIUrl":"https://doi.org/10.1109/ICRMS55680.2022.9944579","url":null,"abstract":"The mixture Weibull distribution is widely used in modeling lifetimes in reliability engineering. Due to the real-time maintenance and the replacement during operating environments, the field failure data are often progressively censored. The classical parameter estimation methods are not available for the lifetime data affected by both multiple failure modes and progressively censoring. This paper proposes an improved stochastic expectation-maximization (SEM) method based on the whale optimization algorithm (WOA). The method consists of two steps. The S-step aims to generate progressively censored data corresponding to each failure data by current parameters estimates. The M-step aims to maximize the $Q$ function formed by the expanded data to obtain new parameters estimates. The WOA is used to optimize the $Q$ function for optimal parameters estimates instead of the complex analytical process of maximization. A numerical example and a real-world dataset are carried out. The results demonstrate the accuracy and applicability of the proposed method in parameter estimations and data fitting.","PeriodicalId":421500,"journal":{"name":"2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130322530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Predictive Replacement Management and Spare Part Planning of High-speed Railway Bearing Under Bayesian Framework 贝叶斯框架下高速铁路轴承联合预测更换管理与备件规划

2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS) Pub Date : 2022-08-21 DOI: 10.1109/ICRMS55680.2022.9944605

Ruoran Han, Li Yang, Longyan Tan, Miaomiao Wang, Xiaobing Ma, Zirong Wang

引用次数: 0