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A lightweight diagnosis method for gear fault based on multi-path convolutional neural networks with attention mechanism

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Abstract

The fault diagnosis of gear is indeed a crucial aspect of maintaining rotating machinery, as it helps in ensuring the safe and efficient operation of industrial equipment. Deep learning models have gained significant attention for gear fault diagnosis due to their ability to automatically extract features from raw data, but they also come with their own set of challenges. One major limitation of existing methods is the insufficient consideration given to the impact of environmental noise at industrial field on the diagnostic effectiveness of the models. Additionally, there is a contradiction between the week computational resources of current embedded platforms for industrial field device applications and the large number of parameters and computations required for deep learning models. This may hinder the deployment of complex models in industrial field devices. To address these issues, a novel approach to multi-path convolutional neural network with dual branch attention (AMPCNN) has been proposed. This approach aims to enhance the recognition of different fault types and maintain high accuracy in noisy environments by extracting multi-scale features of the original vibration signal using multi-path convolution and dual branch attention mechanisms. Furthermore, a multi-knowledge distillation (MKD) method has been introduced to construct lightweight multi-sensor gear fault diagnosis models. This approach facilitates the transfer of multiple knowledge from a complex teacher network to a simpler student network, resulting in a lightweight model that exhibits excellent robustness in various noise environments. The experimental results show that the lightweight model achieves high accuracy while requiring significantly fewer floating-point operations and parameter quantities compared to the original teacher network.

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All authors contributed to the study conception and design. The Implementations, Material preparation, data collection were performed by T.M. Chen, Y.L. Jiang, J.C. Yao, and M. Li. The first draft of the manuscript was written by T.M. Chen and thanks to M.Y. Wang for his revision of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Manyi Wang.

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Chen, T., Wang, M., Jiang, Y. et al. A lightweight diagnosis method for gear fault based on multi-path convolutional neural networks with attention mechanism. Appl Intell 55, 114 (2025). https://doi.org/10.1007/s10489-024-06094-6

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