Sequential Condition Diagnosis for Centrifugal Pump System Using Fuzzy Neural Network

Abstract — 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.