Plunger Pump Fault Diagnosis Method Based on Wavelet Convolution and Multi-Head Self-Attention Mechanism for Multi-Channel Feature Fusion

To address the issues of low accuracy, high dependence on prior knowledge, and poor adaptability in fusing multi-channel features in existing plunger pump fault diagnosis methods, a new method based on single-channel wavelet convolution and multi-head self-attention mechanism is proposed. This method first applies wavelet decomposition and 2D convolution to extract local features of each channel signal individually, and then utilizes the multi-head self-attention mechanism to enhance inter-channel mutual information perception, enabling intelligent diagnosis of plunger pump conditions. Experimental results show that this method can effectively diagnose plunger pump faults with an accuracy of 99.54%, outperforming other deep learning models in terms of training efficiency and diagnostic accuracy.