Bearings Intelligent Fault Diagnosis by 1-D Adder Neural Networks

Jian  Tang; Chao Wei; Quanchang  Li; Yinjun  Wang; Xiaoxi  Ding; Wenbin Huang

doi:10.37965/jdmd.2022.30

Authors

Jian Tang College of Mechanical Engineering, Chongqing University, China https://orcid.org/0000-0001-6658-9127
Chao Wei College of Mechanical Engineering, Chongqing University, China https://orcid.org/0000-0003-4919-7177
Quanchang Li Department of Mechanical and Materials Engineering, Queen's University, Canada https://orcid.org/0000-0001-8011-4988
Yinjun Wang College of Mechanical Engineering, Chongqing University, China https://orcid.org/0000-0001-6749-9203
Xiaoxi Ding College of Mechanical Engineering, Chongqing University, China
Wenbin Huang Chongqing university https://orcid.org/0000-0002-5422-6481

DOI:

https://doi.org/10.37965/jdmd.2022.30

Keywords:

intelligent bearings, convolutional neural network, adder neural network, l1-norm distance, fault diagnosis

Abstract

Integrated with sensors, processors and RF communication modules, intelligent bearing could achieve the autonomous perception and autonomous decision-making, guarantying the safety and reliability during their use. However, because of the resource limitations of the end device, processors in the intelligent bearing are unable to carry the computational load of deep learning models like convolutional neural network (CNN), which involves a great amount of multiplicative operations. To minimize the computation cost of the conventional CNN, based on the idea of AdderNet, a 1-D adder neural network with a wide first-layer kernel (WAddNN) suitable for bearing fault diagnosis is proposed in this paper. The proposed method uses the l₁-norm distance between filters and input features as the output response, thus making the whole network almost free of multiplicative operations. The whole model takes the original signal as the input, uses a wide kernel in the first adder layer to extract features and suppress the high frequency noise, then uses two layers of small kernels for nonlinear mapping. Through experimental comparison with CNN models of the same structure, WAddNN is able to achieve a similar accuracy as CNN models with significantly reduced computational cost. The proposed model provides a new fault diagnosis method for intelligent bearings with limited resources.

Conflict of Interest Statement
The authors declare no conflicts of interest.