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Abstract: The diagnosability problems of the large-scale complex
electromechanical systems are studied, and the main influencing factors
and evaluation criteria of the fault diagnosability are proposed, then
the applications of Hilbert/Huang time-frequency analysis to fault
diagnosis are investigated in order to improve the quality of fault
information. Subsequently, to correctly recognize some unknown faults,
some unsupervising learning algorithms such as self-organizing feature
maps, generative topographic mapping and curvilinear component analysis
are used to classify different working modes of the large-scale complex
electromechanical systems. Finally, based on the latest research finds
in machine learning, to extract nonlinear features from fault signals
and to deal with the problems of non-separated mechanical faults, some
kernel-based methods are presented, and an exploratory study for machine
running trend analysis using kernel methods has been executed.
Key words: Fault diagnosis Condition monitoring Diagnosability
Unsupervised learning Kernel methods
CLC No: TH17
纪念《机械工程学报》创刊50周年——“机械工程技术的历史、进展与展望”主题征文. 国家重大基础研究项目基金(G1998020320)和湖北省自然科学基金(2000j125)资助项目.
Received 20030620, received in revised form 20030716
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