Intelligent fault diagnosis system based on big data. Issue 23 (24th October 2019)

• Record Type: Journal Article
• Authors: Wu, Tianshu; Chen, Shuyu; Wu, Peng
• Is Part Of: Journal of engineering. Volume 2019:Issue 23(2019)
• Page Start: 8980
• Page End: 8985
• Publication Date: 2019-10-24
• Subjects: computerised monitoring -- maintenance engineering -- condition monitoring -- feature extraction -- cloud computing -- learning (artificial intelligence) -- data mining -- production engineering computing -- Big Data -- fault diagnosis; equipment operation big data -- data mining -- machine‐learning algorithm -- complex equipment -- life‐cycle health monitoring -- actual problems -- intelligent fault diagnosis system -- proactive maintenance system -- intelligent fault prediction -- intelligent diagnosis -- operation faults -- hybrid intelligent algorithm -- uncertain fault prediction method -- equipment health condition -- remote diagnosis -- intelligent online monitoring -- diagnosis rules -- expert knowledge base; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2018.9162
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : In view of the actual problems existing in life‐cycle health monitoring and diagnosis of large complex equipment, the machine‐learning algorithm is applied to data mining of the equipment operation big data, the expert knowledge base is established, the diagnosis rules related to the fault are obtained, the intelligent online monitoring and remote diagnosis of the equipment health condition are realised. The system uses uncertain fault prediction method and hybrid intelligent algorithm to discover the hierarchical association between operation feature big data and operation faults, the feature extraction of operation faults, and the intelligent diagnosis of operation faults. It effectively improved the sensitivity, robustness, and accuracy of monitoring and diagnosis. In the cloud service platform based on the Internet of things, the system realises the intelligent fault prediction and diagnosis, establishes a proactive maintenance system, improves the production efficiency, and ensures the production safety.