Dissertation > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory

Research of the Fault Testing and Diagnosis Methods Based on Data Mining

Author SongBin
Tutor LiXiaoPing
School Xi'an University of Electronic Science and Technology
Course Circuits and Systems
Keywords Fault Diagnosis Data Mining Virtual instrument Information Fusion System implementation
Type Master's thesis
Year 2009
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This thesis in data mining, virtual instruments and information fusion theory based on depth study of complex systems fault diagnosis method and its application . The study of large complex systems efficiently , safe, stable and reliable operation , improve of equipment production efficiency and management level , the maximum extent of recovery faulty equipment and restore the economic losses caused by the failure to meet the sound and fast economic development has is of great practical significance . The complete fault data collection experiment based on virtual instrument designed in this paper provide sufficient data for a follow-up study . For complex systems , focusing on rough sets algorithm of attribute reduction and feature extraction , the application of the decision tree algorithm in mining malfunction rules and clustering algorithm incremental data mining application of the new fault rules . By improving rough set attribute reduction algorithm to solve the problem of attribute reduction in less efficient ; directly the fault database for efficient data queries and processing , greatly improving the the ID3 algorithm efficiency and can achieve through the use of embedded SQL ; by the use of the the ART2 algorithm with K-means algorithm combined method effectively inhibited ART2 drift of the cluster center . Information fusion fault diagnosis , diagnostic data fusion , making the diagnosis more comprehensive data ; integration of diagnostic methods , makes diagnosis more rapid , accurate and reliable results . Finally, this three algorithms as the core , the hydraulic system fault diagnosis application background test of a fault diagnosis system design and implementation . Comprehensive use of various functional modules to achieve fault data collection , storage , pretreatment , rules mining, fault diagnosis and report generation print a series of functions . Equipment failure diagnosis experiment , it is proved that the system can accurately carry out fault diagnosis , while rapid self-learning ability to discover new rules , to better meet the needs of the actual fault diagnosis .

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