Reciprocating Pump Fault Diagnosis Using Enhanced Deep Learning Model with Hybrid Attention Mechanism and Dynamic Temporal Convolutional Networks
Year: 2025
Type: article
Abstract: Fault diagnosis is critical for ensuring the reliability of reciprocating pumps in industrial settings. However, challenges such as strong noise interference and unbalanced conditions of existing methods persist. To address these issues, this paper proposes a novel fusion framework integrating a multiple-branch residual module and a hybrid attention module for reciprocating pump fault diagnosis. The framework introduces a multiple-branch residual module with parallel depth-wise separable convolution, dilated convolution, and direct mapping paths to capture complementary features across different scales. A hybrid attention module is designed to achieve adaptive fusion of channel and spatial attention information while reducing computational overhead through learnable gate mechanisms. Experimental validation on the reciprocating pump dataset demonstrates that the proposed framework outperforms existing methods, achieving an average diagnostic accuracy exceeding 98% even in low signal-to-noise ratio (SNR = −3 dB) environments. This research provides a new perspective for mechanical fault diagnosis, offering significant advantages in diagnostic accuracy, robustness, and industrial applicability.
Source: Processes
DOI: 10.3390/pr13123786
Authors: Liming Zhang, Yanlong Xu, Tian Tan, Ling Chen, Xiangyu Guo
Institution: Naval University of Engineering
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4416574288