ON LINE CONDITION MONITORING AND FAULT DIAGNOSIS FOR HYDRAULIC PUMP BASED ON BP ALGORITHM
Dong Xuanming\ Qiu Lihua\ Wang Zhanlin (Beijing University of Aeronautics and Astronautics,Dept.of Automatic Control)
This paper considers five time domain features of pump vibration: P,P p ,R rms ,V var and C crest as minimum combination of diagnosic parameters(MCDP), and uses BP neural networks to fuse and synthesize these features. A on line NN based condition monitoring and fault diagnosic system(NNCMFDS) for hydraulic pump is presented. The paper also discusses two modes of data representations which are single node data mapping(SNDM) and spread encoding(SE).The simulation and bench test results demonstrate that NNCMFDS has a high on line monitoring and fault diagnosic success rate, and NNCMFDS in SE mode has a faster learning rate,more sufficient accuracy and stronger noise reduction capacity than that in SNDM mode.