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Lu, C., Wang, S. and Wang, X. (2017) A Multi-Source Information Fusion Fault Diagnosis for Aviation Hydraulic Pump Based on the New Evidence Similarity Distance. Aerospace Science and Technology, 71, 392-401. https://doi.org/10.1016/j.ast.2017.09.040

has been cited by the following article:

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

  AUTHORS: Pu Zhou, Zhichun Qian, Dongliang Fu, Yi Zhang

  KEYWORDS: Plunger Pump Fault Diagnosis, Wavelet Decomposition, Multi-Head Self-Attention Mechanism, Multi-Channel Fusion

  JOURNAL NAME: Journal of Computer and Communications, Vol.13 No.7, July 30, 2025

  ABSTRACT: 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.