Pub Date : 2022-06-01DOI: 10.1016/j.ress.2022.108383
K. Nguyen, K. Medjaher, C. Gogu
{"title":"Probabilistic deep learning methodology for uncertainty quantification of remaining useful lifetime of multi-component systems","authors":"K. Nguyen, K. Medjaher, C. Gogu","doi":"10.1016/j.ress.2022.108383","DOIUrl":"https://doi.org/10.1016/j.ress.2022.108383","url":null,"abstract":"","PeriodicalId":21122,"journal":{"name":"Reliab. Eng. Syst. Saf.","volume":"27 1","pages":"108383"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77142547","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}
Pub Date : 2022-05-30DOI: 10.48550/arXiv.2205.14950
W. Yeh
Current real-life applications of various networks such as utility (gas, water, electric, 4G/5G) networks, the Internet of Things, social networks, and supply chains. Reliability is one of the most popular tools for evaluating network performance. The fundamental structure of these networks is a binary state network. Distinctive methods have been proposed to efficiently assess binary-state network reliability. A new algorithm called QB-II (quick binary-addition tree algorithm II) is proposed to improve the efficiency of quick BAT, which is based on BAT and outperforms many algorithms. The proposed QB-II implements the shortest minimum cuts (MCs) to separate the entire BAT into main-BAT and sub-BATs, and the source-target matrix convolution products to connect these subgraphs intelligently to improve the efficiency. Twenty benchmark problems were used to validate the performance of the
当前各种网络的实际应用，如公用事业(燃气、水、电、4G/5G)网络、物联网、社交网络和供应链。可靠性是评估网络性能最流行的工具之一。这些网络的基本结构是二进制状态网络。人们提出了不同的方法来有效地评估二元状态网络的可靠性。为了提高快速二叉加法树算法的效率，提出了一种新的算法QB-II (quick binary-addition tree algorithm II)，该算法基于快速二叉加法树算法，并且优于许多算法。本文提出的QB-II算法利用最小割量(MCs)将整个BAT划分为主BAT和子BAT，并利用源-目标矩阵卷积积智能地连接这些子图以提高效率。使用了20个基准问题来验证该方法的性能
{"title":"QB-II for Evaluating the Reliability of Binary-State Networks","authors":"W. Yeh","doi":"10.48550/arXiv.2205.14950","DOIUrl":"https://doi.org/10.48550/arXiv.2205.14950","url":null,"abstract":" Current real-life applications of various networks such as utility (gas, water, electric, 4G/5G) networks, the Internet of Things, social networks, and supply chains. Reliability is one of the most popular tools for evaluating network performance. The fundamental structure of these networks is a binary state network. Distinctive methods have been proposed to efficiently assess binary-state network reliability. A new algorithm called QB-II (quick binary-addition tree algorithm II) is proposed to improve the efficiency of quick BAT, which is based on BAT and outperforms many algorithms. The proposed QB-II implements the shortest minimum cuts (MCs) to separate the entire BAT into main-BAT and sub-BATs, and the source-target matrix convolution products to connect these subgraphs intelligently to improve the efficiency. Twenty benchmark problems were used to validate the performance of the","PeriodicalId":21122,"journal":{"name":"Reliab. Eng. Syst. Saf.","volume":"43 1","pages":"108953"},"PeriodicalIF":0.0,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79318280","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}
Pub Date : 2022-04-29DOI: 10.48550/arXiv.2204.14068
Katharina Rombach, Gabriel Michau, Olga Fink
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.
{"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}
Pub Date : 2022-04-07DOI: 10.48550/arXiv.2204.03717
H. Bao, Hongbin Zhang, T. Shorthill, Edward Chen, Svetlana Lawrence
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%.
{"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}
Pub Date : 2022-04-01DOI: 10.1016/j.ress.2022.108482
Ana González-Muñiz, Ignacio Díaz Blanco, A. Cuadrado, Diego García-Pérez
{"title":"Health indicator for machine condition monitoring built in the latent space of a deep autoencoder","authors":"Ana González-Muñiz, Ignacio Díaz Blanco, A. Cuadrado, Diego García-Pérez","doi":"10.1016/j.ress.2022.108482","DOIUrl":"https://doi.org/10.1016/j.ress.2022.108482","url":null,"abstract":"","PeriodicalId":21122,"journal":{"name":"Reliab. Eng. Syst. Saf.","volume":"18 1","pages":"108482"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74868028","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}
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