Under complex working conditions,it is easy to lead to a failure at the drilling pump fluid end.Traditional fault diagnosis methods are difficult to meet the requirements of the drilling process.In this paper,aiming at the fault diagnosis of five-cylinder drilling pumps,a deep neural network-based fluid end fault diagnosis research was carried out,a CNN-LSTM fault diagnosis model structure was designed,and the effect of LSTM on the performance of the fault diagnosis model was investigated.The results show that the CNN-LSTM model proposed in this paper realizes fast and accurate diagnosis of 9 types of faults under multiple working conditions at the drilling pump fluid end,and by applying the LSTM structure,the accuracy of fault diagnosis model is improved by 7.85％to 97.67％.The CNN-LSTM fault diagnosis model proposed in this paper provides an efficient and accurate fault diagnosis method for the drilling pump fluid end of the drilling process.
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