Qi Zhao, Wenfeng Zhang, Yuhao Deng, Hongbo Zhao, W. Feng
{"title":"基于两步a *搜索法的强故障模型诊断","authors":"Qi Zhao, Wenfeng Zhang, Yuhao Deng, Hongbo Zhao, W. Feng","doi":"10.1109/ICPHM.2019.8819391","DOIUrl":null,"url":null,"abstract":"Many model-based diagnosis researches, such as conflict directed A* search, have been made on weak-fault models, which have no fault mode behavior. However, in the real world, behaviors of common fault modes are usually known. In this situation, strong-fault models are built. Compared with weak-fault models, it is difficult to diagnose strong-fault models because their mode space is greater and non-monotonic. To diagnose strong-fault models efficiently, this paper proposes a two-step A* search, based on the conflict directed A* search. In our method, the consistency over modes, observations and models, is tested by assumption-based truth maintenance system, which generates multiple conflict sets if a fault occurs. Then fault isolation and identification are accomplished separately: firstly, possibly faulty components are isolated based on the conflict sets from the truth maintenance system; then different mode combinations of the faulty components are tested to obtain the specific fault modes. A* search is employed in both steps, as indicated by the name. By separating isolation and identification in two stages, memory and time requirements are reduced significantly. In the case study, a heat control system is utilized to demonstrate the proposed approach.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Diagnosing Strong-fault Models with a Two-step A* Search Method\",\"authors\":\"Qi Zhao, Wenfeng Zhang, Yuhao Deng, Hongbo Zhao, W. Feng\",\"doi\":\"10.1109/ICPHM.2019.8819391\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Many model-based diagnosis researches, such as conflict directed A* search, have been made on weak-fault models, which have no fault mode behavior. However, in the real world, behaviors of common fault modes are usually known. In this situation, strong-fault models are built. Compared with weak-fault models, it is difficult to diagnose strong-fault models because their mode space is greater and non-monotonic. To diagnose strong-fault models efficiently, this paper proposes a two-step A* search, based on the conflict directed A* search. In our method, the consistency over modes, observations and models, is tested by assumption-based truth maintenance system, which generates multiple conflict sets if a fault occurs. Then fault isolation and identification are accomplished separately: firstly, possibly faulty components are isolated based on the conflict sets from the truth maintenance system; then different mode combinations of the faulty components are tested to obtain the specific fault modes. A* search is employed in both steps, as indicated by the name. By separating isolation and identification in two stages, memory and time requirements are reduced significantly. In the case study, a heat control system is utilized to demonstrate the proposed approach.\",\"PeriodicalId\":113460,\"journal\":{\"name\":\"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPHM.2019.8819391\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPHM.2019.8819391","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Diagnosing Strong-fault Models with a Two-step A* Search Method
Many model-based diagnosis researches, such as conflict directed A* search, have been made on weak-fault models, which have no fault mode behavior. However, in the real world, behaviors of common fault modes are usually known. In this situation, strong-fault models are built. Compared with weak-fault models, it is difficult to diagnose strong-fault models because their mode space is greater and non-monotonic. To diagnose strong-fault models efficiently, this paper proposes a two-step A* search, based on the conflict directed A* search. In our method, the consistency over modes, observations and models, is tested by assumption-based truth maintenance system, which generates multiple conflict sets if a fault occurs. Then fault isolation and identification are accomplished separately: firstly, possibly faulty components are isolated based on the conflict sets from the truth maintenance system; then different mode combinations of the faulty components are tested to obtain the specific fault modes. A* search is employed in both steps, as indicated by the name. By separating isolation and identification in two stages, memory and time requirements are reduced significantly. In the case study, a heat control system is utilized to demonstrate the proposed approach.