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Single Fault Diagnosis Method of Sensors in Cascade System Based on Data-Driven.
Na, Wenbo; Guo, Siyu; Gao, Yanfeng; Yang, Jianxing; Huang, Junjie.
Afiliação
  • Na W; College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
  • Guo S; College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
  • Gao Y; College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
  • Yang J; College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
  • Huang J; College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
Sensors (Basel) ; 21(21)2021 Nov 04.
Article em En | MEDLINE | ID: mdl-34770645
The reliability and safety of the cascade system, which is widely applied, have attached attention increasingly. Fault detection and diagnosis can play a significant role in enhancing its reliability and safety. On account of the complexity of the double closed-loop system in operation, the problem of fault diagnosis is relatively complex. For the single fault of the second-order valued system sensors, a real-time fault diagnosis method based on data-driven is proposed in this study. Off-line data is employed to establish static fault detection, location, estimation, and separation models. The static models are calibrated with on-line data to obtain the real-time fault diagnosis models. The real-time calibration, working flow and anti-interference measures of the real-time diagnosis system are given. Experiments results demonstrate the validity and accuracy of the fault diagnosis method, which is suitable for the general cascade system.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Reprodutibilidade dos Testes Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Reprodutibilidade dos Testes Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article