The Technology And Realization Of Intelligent Fault Diagnosis For The Pump Valves Of Reciprocating Pump

Urgent requirement of market has promoted the improvement of the technology of mechanical fault diagnosis (FD). So far, many methods have been proposed in the field of fault diagnosis. But practical application is insufficient, and less in the application of industrial devices. Two key issues of fault diagnosis for the pump valves of reciprocating pump are extracting the fault feature information of stationary process efficiently from system feature signals and identifying the specific faults correctly with analysis of causes.At present, usual method of FD is measuring vibration signal as system feature signals for fault characteristic eigenvectors pickups. Although the technology of vibration diagnosis has acquired some achievement, on the occasion of having several faults , the method has some trouble that cannot be solved . To solve this problem, some methods i.e. rough-set theory and genetic algorithm have to be used to identify specific fault and decide which pump valve has fault.The paper creatively proposes the idea ofusing ordinary pressure signals;(pressure in valve boxes) as system feature signal to pickup fault characteristic eigenvectors and verifies the correction of the method through self-correlation analysis in the time-zone and power spectral and wavelet packet analysis in the frequency-zone. What's more, it is not easy to be disturbed by outside environment for requiring the signals from the inside of reciprocating pump cylinder. The method has special obvious advantages to diagnosis the faults when several faults exist simultaneously. The paper constructs a three-layer forward neural network to diagnosis the fault and trains the network with characteristic eigenvectors extracted through the wavelet packet analysis , when training the net using the method of adding inertia item and the BP algorithm of conjugate gradient method, at the same time adapt the initial data of the network through the test. After confirm the construction and parameters of the net, the result is valid and justified through the verification using the test sample data.The paper develops the intelligent fault diagnosis system regarded/the fault diagnosis factor as the center of the whole system. The main function of the data collection system developed by VC++ program is collecting field data and managing the data. And the fault diagnosis system on the basis of Matlab software platform pick up the fault characteristic eigenvectors through the method of wavelet packet analysis and realize the fault diagnosis using the technology of neural network.