Fault diagnosis of progressing cavity pump well based on wavelet package and Elman neural network

In this paper, the fault diagnosis problem is studied for progressing cavity pump well base on wavelet package and Elman neural network. The signals of active power can fully reflect the status of progressing cavity pump wells. A new fault diagnosis method for cavity pump wells is presented. This method uses wavelet time-frequency analysis technology for de-noising and filtering of active power signals, uses 3-layer db4 wavelet packet to decomposition fault signal of different frequencies, extracts fault feature based on changes in band power spectrum, then use Elman neural network to identify the fault. By use of Matlab simulation, the results show that this method can effectively improve the diagnostic accuracy of progressing cavity pump wells.