摘要：A mud pump is one of the three key components of a drilling site, and its lifetime and reliability are related to safety and cost. The fluid end is the most easily damaged part of the mud pump. To ensure normal operation, the fault modes of the fluid end need to be effectively identified. This study proposes to employ acoustic emission technique to identify the fault modes of mud pump fluid end, including valve disk leakage, spring break, and piston wear. The analysis method of parameters and waveforms of the acoustic emission signals are both used in this article. The characteristic parameters of the acoustic emission signals are combined with grey relational analysis to identify the fault modes with small samples; wavelet packet signal processing technique is applied to decompose and reconstruct the obtained acoustic emission signal waveforms and extract the energy of each frequency range to construct the eigenvectors which are input into the genetic algorithm with back propagation neural network for fault diagnosis. The results show that the fault modes under small samples can be effectively identified by combining the characteristic parameters and the grey relational analysis; the frequency range energy eigenvalue can be extracted using the method of wavelet packet signal processing, and the genetic algorithm with back propagation neural network is of better convergence than that of the back propagation neural network.
关键词：Mud pump fluid end; fault diagnosis; acoustic emission; grey relational analysis; wavelet packet; genetic algorithm with back propagation neural network
