LEFE-Net: A Lightweight Efficient Feature Extraction Network With Strong Robustness for Bearing Fault Diagnosis

High precision and fast fault diagnosis is an important guarantee for the safe and reliable operation of machinery. In recent years, due to the strong recognition ability, data-driven fault diagnosis technology based on deep learning has attracted enormous attention. The fault diagnosis module propo...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2021, Vol.70, p.1-11
Main Authors: Fang, Hairui, Deng, Jin, Zhao, Bo, Shi, Yan, Zhou, Jianye, Shao, Siyu
Format: Article
Language:English
Subjects:
Artificial neural networks
Bearing fault diagnosis
Convolution
Convolutional codes
convolutional neural networks (CNN)
dynamic convolution
Fault diagnosis
Feature extraction
Feature maps
Integrated circuits
Kernel
Lightweight
lightweight network
Load modeling
Machine learning
Recognition
spatial attention mechanism
Two dimensional models
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Summary:High precision and fast fault diagnosis is an important guarantee for the safe and reliable operation of machinery. In recent years, due to the strong recognition ability, data-driven fault diagnosis technology based on deep learning has attracted enormous attention. The fault diagnosis module proposed by many scholars has achieved excellent recognition results, but some of them are too complex to deploy in practice, due to the high costs. In this article, an efficient feature extraction method based on the convolutional neural networks (CNN) was proposed, and the high-precision fault diagnosis task was completed used a lightweight network. First, a 1-D CNN (1-D-CNN) is used to extract the multichannel features for the input original signals, to generate the feature maps which are highly consistent with the 2-D CNN (2-D-CNN). Second, a strategy of the lightweight model is proposed based on the principle of dynamic convolution and separable convolution. Third, a spatial attention mechanism (SAM) is used to adjust the weights of the output feature maps. Finally, the model completes the diagnosis task through the learned features. The performance of the model was verified under different operating conditions and noise environments. The experimental results demonstrate that the proposed method has excellent anti-noise ability and domain adaptability.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2021.3067187