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Abstract: In order to solve the problems of correctly identifying
incipient fault and accurately monitoring fault development for
electromechanical equipment, a new method of incipient fault intelligent
monitoring and diagnosis based on fuzzy support vector data
description(FSVDD) is proposed. With this method, one-class classifier
can be built when only the information of the target class is available,
and the outlier objects can be hierarchically distinguished from target
objects when these membership degrees of outlier objects are appended to
the kernel function. The proposed method is applied to the condition
monitoring and fault diagnosis of electromechanical equipment, which can
detect incipient fault only using normal condition signals and identify
the fault severity. The experimental result shows that this method not
only fast detects the bearing incipient fault, but accurately identifies
the fault severity.
Key words: Fuzzy support vector data description One-class
classification Incipient fault Intelligent monitoring and diagnosis
CLC No: TH17
TP18
国家自然科学基金重点(50335030)、国家自然科学基金(50175087，50305012)、国家重点基础研究发展计划(973计划)(2005CB724106)和高校博士点基金(20040698026)资助项目.
Received 20050225, received in revised form 20050727
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