An Adaptive Weighted Multiscale Convolutional Neural Network for Rotating Machinery Fault Diagnosis Under Variable Operating Conditions
Extracting robust fault sensitive features of vibration signals remains a challenge for rotating machinery fault diagnosis under variable operating conditions. Most existing fault diagnosis methods based on the convolutional neural network (CNN) can only extract single-scale features, which not only...
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doaj-d7c80cfef3a846118454f91a92acb9852021-03-30T00:03:17ZengIEEEIEEE Access2169-35362019-01-01711895411896410.1109/ACCESS.2019.29366258808927An Adaptive Weighted Multiscale Convolutional Neural Network for Rotating Machinery Fault Diagnosis Under Variable Operating ConditionsHuihui Qiao0https://orcid.org/0000-0001-7767-655XTaiyong Wang1https://orcid.org/0000-0002-7820-7699Peng Wang2Lan Zhang3Mingda Xu4School of Mechanical Engineering, Tianjin University, Tianjin, ChinaSchool of Mechanical Engineering, Tianjin University, Tianjin, ChinaSchool of Mechanical Engineering, Tianjin University, Tianjin, ChinaSchool of Mechanical Engineering, Tianjin University, Tianjin, ChinaSchool of Mechanical Engineering, Tianjin University, Tianjin, ChinaExtracting robust fault sensitive features of vibration signals remains a challenge for rotating machinery fault diagnosis under variable operating conditions. Most existing fault diagnosis methods based on the convolutional neural network (CNN) can only extract single-scale features, which not only loss fault sensitive information on other scales, but also suffer from the domain shift problem. In this work, a novel end-to-end deep learning network named adaptive weighted multiscale convolutional neural network (AWMSCNN) is proposed to adaptively extract robust and discriminative multiscale fusion features from raw vibration signals. The AWMSCNN consists of three main components: the denoising layer, the adaptive weighted multiscale convolutional (AWMSC) block, and the multiscale feature fusion layer. The AWMSC block can learn rich and complementary features on multiple scales in parallel. Then, an adaptive weight vector is introduced to modulate multiscale features to emphasize fault sensitive features and suppress features that are sensitive to operating conditions. The train wheelset bearing dataset and the bearing dataset provided by Case Western Reserve University (CWRU) are used to verify the superiority of the proposed model over the basic CNN and other multiscale CNN models. The experiment results show that the proposed model has strong fault discriminative ability and domain adaptive ability against variable operating conditions.https://ieeexplore.ieee.org/document/8808927/Adaptive weighted multiscale feature learningconvolutional neural networkdeep learningfault diagnosisrotating machineryvariable operating conditions |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Huihui Qiao Taiyong Wang Peng Wang Lan Zhang Mingda Xu |
spellingShingle |
Huihui Qiao Taiyong Wang Peng Wang Lan Zhang Mingda Xu An Adaptive Weighted Multiscale Convolutional Neural Network for Rotating Machinery Fault Diagnosis Under Variable Operating Conditions IEEE Access Adaptive weighted multiscale feature learning convolutional neural network deep learning fault diagnosis rotating machinery variable operating conditions |
author_facet |
Huihui Qiao Taiyong Wang Peng Wang Lan Zhang Mingda Xu |
author_sort |
Huihui Qiao |
title |
An Adaptive Weighted Multiscale Convolutional Neural Network for Rotating Machinery Fault Diagnosis Under Variable Operating Conditions |
title_short |
An Adaptive Weighted Multiscale Convolutional Neural Network for Rotating Machinery Fault Diagnosis Under Variable Operating Conditions |
title_full |
An Adaptive Weighted Multiscale Convolutional Neural Network for Rotating Machinery Fault Diagnosis Under Variable Operating Conditions |
title_fullStr |
An Adaptive Weighted Multiscale Convolutional Neural Network for Rotating Machinery Fault Diagnosis Under Variable Operating Conditions |
title_full_unstemmed |
An Adaptive Weighted Multiscale Convolutional Neural Network for Rotating Machinery Fault Diagnosis Under Variable Operating Conditions |
title_sort |
adaptive weighted multiscale convolutional neural network for rotating machinery fault diagnosis under variable operating conditions |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Extracting robust fault sensitive features of vibration signals remains a challenge for rotating machinery fault diagnosis under variable operating conditions. Most existing fault diagnosis methods based on the convolutional neural network (CNN) can only extract single-scale features, which not only loss fault sensitive information on other scales, but also suffer from the domain shift problem. In this work, a novel end-to-end deep learning network named adaptive weighted multiscale convolutional neural network (AWMSCNN) is proposed to adaptively extract robust and discriminative multiscale fusion features from raw vibration signals. The AWMSCNN consists of three main components: the denoising layer, the adaptive weighted multiscale convolutional (AWMSC) block, and the multiscale feature fusion layer. The AWMSC block can learn rich and complementary features on multiple scales in parallel. Then, an adaptive weight vector is introduced to modulate multiscale features to emphasize fault sensitive features and suppress features that are sensitive to operating conditions. The train wheelset bearing dataset and the bearing dataset provided by Case Western Reserve University (CWRU) are used to verify the superiority of the proposed model over the basic CNN and other multiscale CNN models. The experiment results show that the proposed model has strong fault discriminative ability and domain adaptive ability against variable operating conditions. |
topic |
Adaptive weighted multiscale feature learning convolutional neural network deep learning fault diagnosis rotating machinery variable operating conditions |
url |
https://ieeexplore.ieee.org/document/8808927/ |
work_keys_str_mv |
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1724188708553359360 |