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research-article

Adaptive multi-scale attention convolution neural network for cross-domain fault diagnosis

Authors:

Chang-Soo KimAuthors Info & Claims

Volume 236, Issue C

https://doi.org/10.1016/j.eswa.2023.121216

Published: 01 February 2024 Publication History

Abstract

This paper proposes a novel approach named adaptive multi-scale attention convolution neural network (AmaCNN) to accurately detect cross-domain faults with very few labelled data. In AmaCNN, multi-scale feature fusion CNN (MSFFCNN) with a multi-level attention scheme (MLAS) extracts multi-scale less-noise features from source and target domains. Considering the domain shift and semantic difference in the two domain features, a cross-domain adaption (CDA) scheme is applied. Significantly, the extracted domain features are measured with correlation alignment (CORAL) distance to minimize the domain shift first. Then, semantic alignment (SA) loss aligns and separates domain-invariant features point-by-point. Therefore, the proposed AmaCNN could learn rich multi-scale, less-noise, domain-invariant, and semantic-alignment features using limited training samples to detect cross- fault accurately. The experimental results on three real data sets confirmed its priority and reliability. Besides, the in-depth analysis has confirmed each component’s effectiveness and CDA’s good generality.

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Cited By

Shao XLi DRa IKim C(2024)DSMT-1DCNNKnowledge-Based Systems10.1016/j.knosys.2024.111609292:COnline publication date: 23-May-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111609
Guo BQiao ZZhang NWang YWu FPeng Q(2024)Attention-based ConvNeXt with a parallel multiscale dilated convolution residual module for fault diagnosis of rotating machineryExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123764249:PCOnline publication date: 17-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123764

Index Terms

Adaptive multi-scale attention convolution neural network for cross-domain fault diagnosis
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 236, Issue C

Feb 2024

1583 pages

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Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 February 2024

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Cited By

Shao XLi DRa IKim C(2024)DSMT-1DCNNKnowledge-Based Systems10.1016/j.knosys.2024.111609292:COnline publication date: 23-May-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111609
Guo BQiao ZZhang NWang YWu FPeng Q(2024)Attention-based ConvNeXt with a parallel multiscale dilated convolution residual module for fault diagnosis of rotating machineryExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123764249:PCOnline publication date: 17-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123764

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