Abstract
Drilling pumps are crucial for oil and gas operations. Timely diagnosis and troubleshooting of fluid end faults is crucial to ensure the safe and stable operation of drilling pumps and prevent further deterioration of faults. Hence, from a data-driven perspective, this study proposes a fault diagnosis method for the fluid end of drilling pumps based on the generalized S transform (GST) and convolutional neural networks (CNN), using the vibration signal of the fluid end. To address the issue of noise pollution in the vibration signal resulting in unclear feature information and difficult feature extraction, the vibration signal is transformed into a time−frequency diagram based on GST, which more accurately characterizes the fault characteristics of the vibration signal. An AlexNet model, improved by introducing batch normalization and optimizing the number of neurons in the fully connected layer, is used to analyze the recognition performance of the model for the normal, minor damage, and severe damage states of the fluid end of the drilling pump. Finally, the diagnosis results are compared to other methods, with the results showing that the proposed method has the highest fault diagnosis accuracy. With an average recognition rate of 99.21% for the nine types of fluid end, the method proposed in this study provides a way to accurately diagnose fluid end failures, thus supporting the safe and efficient operation of drilling pumps.