Reliab. Eng. Syst. Saf.最新文献_第2页

A novel fixed-node unconnected subgraph method for calculating the reliability of binary-state networks 二态网络可靠性计算的一种新的固定节点不连通子图方法

Reliab. Eng. Syst. Saf. Pub Date : 2022-06-01 DOI: 10.2139/ssrn.4027927

Hongjun Cui, Fei Wang, Xinwei Ma, Minqing Zhu

引用次数: 4

A novel dual-stream self-attention neural network for remaining useful life estimation of mechanical systems 一种用于机械系统剩余使用寿命估计的新型双流自关注神经网络

Reliab. Eng. Syst. Saf. Pub Date : 2022-06-01 DOI: 10.1016/j.ress.2022.108444

Danyang Xu, H. Qiu, Liang Gao, Zan Yang, Dapeng Wang

引用次数: 20

Probabilistic deep learning methodology for uncertainty quantification of remaining useful lifetime of multi-component systems 多部件系统剩余使用寿命不确定性量化的概率深度学习方法

Reliab. Eng. Syst. Saf. Pub Date : 2022-06-01 DOI: 10.1016/j.ress.2022.108383

K. Nguyen, K. Medjaher, C. Gogu

引用次数: 23

QB-II for Evaluating the Reliability of Binary-State Networks 二值状态网络可靠性评估的QB-II

Reliab. Eng. Syst. Saf. Pub Date : 2022-05-30 DOI: 10.48550/arXiv.2205.14950

W. Yeh

引用次数: 0

Modeling network vulnerability of urban rail transit under cascading failures: A Coupled Map Lattices approach 级联故障下城市轨道交通网络脆弱性建模:一种耦合映射格方法

Reliab. Eng. Syst. Saf. Pub Date : 2022-05-01 DOI: 10.1016/j.ress.2022.108320

Qingfeng Lu, Lei Zhang, Peng Xu, Xin Cui, Jing Li

引用次数: 30

Controlled Generation of Unseen Faults for Partial and OpenSet&Partial Domain Adaptation 局部和开放域自适应的不可见故障控制生成

Reliab. Eng. Syst. Saf. Pub Date : 2022-04-29 DOI: 10.48550/arXiv.2204.14068

Katharina Rombach, Gabriel Michau, Olga Fink

{"title":"Controlled Generation of Unseen Faults for Partial and OpenSet&Partial Domain Adaptation","authors":"Katharina Rombach, Gabriel Michau, Olga Fink","doi":"10.48550/arXiv.2204.14068","DOIUrl":"https://doi.org/10.48550/arXiv.2204.14068","url":null,"abstract":"New operating conditions can result in a significant performance drop of fault diagnostics models due to the domain shift between the training and the testing data distributions. While several domain adaptation approaches have been proposed to overcome such domain shifts, their application is limited if the fault classes represented in the two domains are not the same. To enable a better transferability of the trained models between two different domains, particularly in setups where only the healthy data class is shared between the two domains, we propose a new framework for Partial and Open-Partial domain adaptation based on generating distinct fault signatures with a Wasserstein GAN. The main contribution of the proposed framework is the controlled synthetic fault data generation with two main distinct characteristics. Firstly, the proposed methodology enables to generate unobserved fault types in the target domain by having only access to the healthy samples in the target domain and faulty samples in the source domain. Secondly, the fault generation can be controlled to precisely generate distinct fault types and fault severity levels. The proposed method is especially suited in extreme domain adaption settings that are particularly relevant in the context of complex and safety-critical systems, where only one class is shared between the two domains. We evaluate the proposed framework on Partial as well as Open-Partial domain adaptation tasks on two bearing fault diagnostics case studies. Our experiments conducted in different label space settings showcase the versatility of the proposed framework. The proposed methodology provided superior results compared to other methods given large domain gaps.","PeriodicalId":21122,"journal":{"name":"Reliab. Eng. Syst. Saf.","volume":"96 1","pages":"108857"},"PeriodicalIF":0.0,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88284696","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: 14

Quantitative Evaluation of Common Cause Failures in High Safety-significant Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants 核电厂高安全重要性安全相关数字仪表和控制系统共因故障的定量评估

Reliab. Eng. Syst. Saf. Pub Date : 2022-04-07 DOI: 10.48550/arXiv.2204.03717

H. Bao, Hongbin Zhang, T. Shorthill, Edward Chen, Svetlana Lawrence

{"title":"Quantitative Evaluation of Common Cause Failures in High Safety-significant Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants","authors":"H. Bao, Hongbin Zhang, T. Shorthill, Edward Chen, Svetlana Lawrence","doi":"10.48550/arXiv.2204.03717","DOIUrl":"https://doi.org/10.48550/arXiv.2204.03717","url":null,"abstract":"Digital instrumentation and control (DI&C) systems at nuclear power plants (NPPs) have many advantages over analog systems. They are proven to be more reliable, cheaper, and easier to maintain given obsolescence of analog components. However, they also pose new engineering and technical challenges, such as possibility of common cause failures (CCFs) unique to digital systems. This paper proposes a Platform for Risk Assessment of DI&C (PRADIC) that is developed by Idaho National Laboratory (INL). A methodology for evaluation of software CCFs in high safety-significant safety-related DI&C systems of NPPs was developed as part of the framework. The framework integrates three stages of a typical risk assessment—qualitative hazard analysis and quantitative reliability and consequence analyses. The quantified risks compared with respective acceptance criteria provide valuable insights for system architecture alternatives allowing design optimization in terms of risk reduction and cost savings. A comprehensive case study performed to demonstrate the framework’s capabilities is documented in this paper. Results show that the PRADIC is a powerful tool capable to identify potential digital-based CCFs, estimate their probabilities, and evaluate their impacts on system and plant safety. FT was quantified with SAPHIRE using a truncation level of 1E-12; RTS failure probability is 4.288E-6 with five cut sets. Results indicate hardware CCFs are the main concerns for the failure analog safety-related redundant I&C systems. Compared with the original RTS-FT, the total failure probability of integrated four-division RTS-FT is reduced about 50%.","PeriodicalId":21122,"journal":{"name":"Reliab. Eng. Syst. Saf.","volume":"4 1","pages":"108973"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86911334","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: 5

Health indicator for machine condition monitoring built in the latent space of a deep autoencoder 机器状态监测健康指示器内置在深度自动编码器的潜在空间中

Reliab. Eng. Syst. Saf. Pub Date : 2022-04-01 DOI: 10.1016/j.ress.2022.108482

Ana González-Muñiz, Ignacio Díaz Blanco, A. Cuadrado, Diego García-Pérez

引用次数: 29

Critical facility accessibility rapid failure early-warning detection and redundancy mapping in urban flooding 城市洪涝灾害中关键设施可达性快速故障预警检测与冗余映射

Reliab. Eng. Syst. Saf. Pub Date : 2022-04-01 DOI: 10.1016/j.ress.2022.108555

Utkarsh Gangwal, Shangjia Dong

引用次数: 12

Bearing remaining useful life prediction with convolutional long short-term memory fusion networks 基于卷积长短期记忆融合网络的轴承剩余使用寿命预测

Reliab. Eng. Syst. Saf. Pub Date : 2022-04-01 DOI: 10.1016/j.ress.2022.108528

Shaoke Wan, Xiaohu Li, Yanfei Zhang, Shijie Liu, Jun Hong, Dongfeng Wang

引用次数: 27