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Indexed by:会议论文

Date of Publication:2013-08-27

Included Journals:EI、CPCI-S、Scopus

Page Number:231-236

Key Words:Data base; Coal-bed methane gathering process; Fault diagnosis; Fuzzy; Association memory model; Self-learning

Abstract:The process of coal-bed methane gathering includes two important parts: coal-bed production and gas compression by booster station. So the fault often occurs in these two parts. In this article, we adopt FAM (Fuzzy Associative Memory) neural network to realize compressor fault diagnosis. This model implements intelligent fault diagnosis and self-learning of the knowledge database. Thus it significantly improves the accuracy and scalability of fault diagnosis system of the reciprocating compressor. The system has been adopted in experiment. In addition, the fault diagnosis system is based on Visual Studio 2008. NET and SQL sever 2005 to monitor operation parameters and equipment status real-timely and to realize fault management and diagnosis. The system has friendly man-machine interface and is more convenient and easy to understand the operation flow, more powerful to store data and more easy to embed fault diagnosis algorithm into it.