期刊名称:Neural Information Processing: Letters and Reviews
电子版ISSN:1738-2532
出版年度:2007
卷号:11
期号:3
页码:41-50
出版社:Neural Information Processing
摘要:This paper proposed a sequential diagnosis method using fuzzy neural network called “partially-linearized
neural network (PNN)”, by which the fault types of rotating machinery can be precisely and effectively
distinguished at an early stage on the basis of the possibilities of symptom parameters. The non-dimensional
symptom parameters (NPS) in time domain are defined for reflecting the features of time signals measured for
the fault diagnosis of rotating machinery. The synthetic detection index (SDI) is also proposed to evaluate the
sensitivity of NSPs for detecting faults. The practical example of condition diagnosis for detecting and distinguishing
fault states of a centrifugal pump system, such as cavitation, impeller damage and unbalance which often occur in
a centrifugal pump system, are shown to verify the efficiency of the method proposed in this paper.