{"title":"基于状态挖掘的信念规则库的液体运载火箭潜在故障诊断","authors":"Feng Han;Zhichao Feng;Bo Mo;Ruohan Yang","doi":"10.1109/TIM.2025.3604108","DOIUrl":null,"url":null,"abstract":"A new latent fault diagnosis (FDs) method is developed for a liquid launch vehicle. The proposed method aims to solve three challenges: lack of failure data, limited expert cognition, and new system latent state triggered by faults. As an interpretable method, the belief rule base (BRB) model can both combine the data and knowledge that can solve the first two problems. It provides a basis for FDs of the vehicle. However, when the vehicle fails, its internal mechanism changes, and the new system state may exist. Limited by the output framework of BRB, it cannot detect these latent faults. Hence, a new BRB with state miner (BRB-M) is proposed with an adaptive discernment framework. It can mine the new system states by the combination of output propositions. Then, the traceability analysis of BRB-M is conducted based on the transparency of its modeling process, and the influence of each input characteristic is analyzed quantitatively. To improve the diagnosis accuracy, an optimization model is put forward for BRB-M. To illustrate the performance of the proposed method, an experiment of vehicle is conducted. In the experiment, the diagnosis accuracy is 97.00%, and increases 29.11%, 23.17% compared with the fuzzy theory and BP neural network.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.9000,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Latent Fault Diagnosis for Liquid Launch Vehicle Using Belief Rule Base With State Miner\",\"authors\":\"Feng Han;Zhichao Feng;Bo Mo;Ruohan Yang\",\"doi\":\"10.1109/TIM.2025.3604108\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new latent fault diagnosis (FDs) method is developed for a liquid launch vehicle. The proposed method aims to solve three challenges: lack of failure data, limited expert cognition, and new system latent state triggered by faults. As an interpretable method, the belief rule base (BRB) model can both combine the data and knowledge that can solve the first two problems. It provides a basis for FDs of the vehicle. However, when the vehicle fails, its internal mechanism changes, and the new system state may exist. Limited by the output framework of BRB, it cannot detect these latent faults. Hence, a new BRB with state miner (BRB-M) is proposed with an adaptive discernment framework. It can mine the new system states by the combination of output propositions. Then, the traceability analysis of BRB-M is conducted based on the transparency of its modeling process, and the influence of each input characteristic is analyzed quantitatively. To improve the diagnosis accuracy, an optimization model is put forward for BRB-M. To illustrate the performance of the proposed method, an experiment of vehicle is conducted. In the experiment, the diagnosis accuracy is 97.00%, and increases 29.11%, 23.17% compared with the fuzzy theory and BP neural network.\",\"PeriodicalId\":13341,\"journal\":{\"name\":\"IEEE Transactions on Instrumentation and Measurement\",\"volume\":\"74 \",\"pages\":\"1-11\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2025-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Instrumentation and Measurement\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11145155/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Instrumentation and Measurement","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11145155/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Latent Fault Diagnosis for Liquid Launch Vehicle Using Belief Rule Base With State Miner
A new latent fault diagnosis (FDs) method is developed for a liquid launch vehicle. The proposed method aims to solve three challenges: lack of failure data, limited expert cognition, and new system latent state triggered by faults. As an interpretable method, the belief rule base (BRB) model can both combine the data and knowledge that can solve the first two problems. It provides a basis for FDs of the vehicle. However, when the vehicle fails, its internal mechanism changes, and the new system state may exist. Limited by the output framework of BRB, it cannot detect these latent faults. Hence, a new BRB with state miner (BRB-M) is proposed with an adaptive discernment framework. It can mine the new system states by the combination of output propositions. Then, the traceability analysis of BRB-M is conducted based on the transparency of its modeling process, and the influence of each input characteristic is analyzed quantitatively. To improve the diagnosis accuracy, an optimization model is put forward for BRB-M. To illustrate the performance of the proposed method, an experiment of vehicle is conducted. In the experiment, the diagnosis accuracy is 97.00%, and increases 29.11%, 23.17% compared with the fuzzy theory and BP neural network.
期刊介绍:
Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.