Classification Fusion for Bearing Fault Diagnosis with Multi-source Domain Shift

Huang Mu-sheng; Wu Song-song; Zheng Shi-yuan; Yu Xi; Zhuang Jia-yang; Jing Xiao-yuan

doi:doi:10.11648/j.acis.20221002.12

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Classification Fusion for Bearing Fault Diagnosis with Multi-source Domain Shift

Huang Mu-sheng, Wu Song-song, Zheng Shi-yuan, Yu Xi, Zhuang Jia-yang, Jing Xiao-yuan

Published in Automation, Control and Intelligent Systems (Volume 10, Issue 2)

Received: 10 May 2022 Published: 12 May 2022

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Abstract

Bearings are one of the most widely used components of rotating machinery, whose failure can cause serious injuries and economic losses, therefore bearing fault diagnosis is an essential step in maintaining the safe and stable operation of industrial processes. Bearing fault diagnosis aims to detect the bearing fault condition and accurately classify it into a fault category based on sensing signals, such as vibration data. In practical applications, bearings always work in different types of equipment and under various working conditions, leading to performance degradation of diagnosis models due to the domain gap between the training data and the test data. Domain adaptation has been developed to address the domain shift problem in bearing fault diagnosis with demonstrated efficacy. Current domain adaptation models focus on the single-source scenario, by ignoring that sensing data may be collected from multiple sources in practical applications, and then be annotated for mode training. In this situation, it is non-trivial to use the single-source domain adaptation model to address the multi-source domain shift problem, because the domain gap exists among the source domains and the target domain. To solve this problem, we propose a novel bearing fault diagnosis model based on classification decision fusion to address the problem of multi-source domain conversion. Firstly, we train a source-aware fault diagnosis model in each source domain and then use it to predict the fault labels of the target samples. Second, a similarity score between each source domain and target domain is computed based on their feature distributions using local discriminant analysis and Maximum Mean Discrepancy. Finally, the similarity scores are used as domain weights in a proposed classification decision fusion strategy that uses a weighted linear combination process of predicted fault labels to provide the final predicted labels for the target samples. The benefits of the adaptive weighting fusion based on the classification result level, which makes full use of the available data from multiple source domains, measures the differences in distribution between the source and target domains and automatically adjust the weights to improve the diagnostic capability of the target domain. The effectiveness of the proposed method for diagnosing bearing faults under different operating and measurement conditions was verified using a bearing data set provided by Case Western Reserve University.

Published in	Automation, Control and Intelligent Systems (Volume 10, Issue 2)
DOI	10.11648/j.acis.20221002.12
Page(s)	17-26
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Multi-source Domain Transfer, Bearing Fault Diagnosis, Classification Fusion, Knowledge Transfer, Cross-domain Fault Diagnosis

References

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APA Style

Huang Mu-sheng, Wu Song-song, Zheng Shi-yuan, Yu Xi, Zhuang Jia-yang, et al. (2022). Classification Fusion for Bearing Fault Diagnosis with Multi-source Domain Shift. Automation, Control and Intelligent Systems, 10(2), 17-26. https://doi.org/10.11648/j.acis.20221002.12

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ACS Style

Huang Mu-sheng; Wu Song-song; Zheng Shi-yuan; Yu Xi; Zhuang Jia-yang, et al. Classification Fusion for Bearing Fault Diagnosis with Multi-source Domain Shift. Autom. Control Intell. Syst. 2022, 10(2), 17-26. doi: 10.11648/j.acis.20221002.12

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AMA Style

Huang Mu-sheng, Wu Song-song, Zheng Shi-yuan, Yu Xi, Zhuang Jia-yang, et al. Classification Fusion for Bearing Fault Diagnosis with Multi-source Domain Shift. Autom Control Intell Syst. 2022;10(2):17-26. doi: 10.11648/j.acis.20221002.12

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@article{10.11648/j.acis.20221002.12,
  author = {Huang Mu-sheng and Wu Song-song and Zheng Shi-yuan and Yu Xi and Zhuang Jia-yang and Jing Xiao-yuan},
  title = {Classification Fusion for Bearing Fault Diagnosis with Multi-source Domain Shift},
  journal = {Automation, Control and Intelligent Systems},
  volume = {10},
  number = {2},
  pages = {17-26},
  doi = {10.11648/j.acis.20221002.12},
  url = {https://doi.org/10.11648/j.acis.20221002.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20221002.12},
  abstract = {Bearings are one of the most widely used components of rotating machinery, whose failure can cause serious injuries and economic losses, therefore bearing fault diagnosis is an essential step in maintaining the safe and stable operation of industrial processes. Bearing fault diagnosis aims to detect the bearing fault condition and accurately classify it into a fault category based on sensing signals, such as vibration data. In practical applications, bearings always work in different types of equipment and under various working conditions, leading to performance degradation of diagnosis models due to the domain gap between the training data and the test data. Domain adaptation has been developed to address the domain shift problem in bearing fault diagnosis with demonstrated efficacy. Current domain adaptation models focus on the single-source scenario, by ignoring that sensing data may be collected from multiple sources in practical applications, and then be annotated for mode training. In this situation, it is non-trivial to use the single-source domain adaptation model to address the multi-source domain shift problem, because the domain gap exists among the source domains and the target domain. To solve this problem, we propose a novel bearing fault diagnosis model based on classification decision fusion to address the problem of multi-source domain conversion. Firstly, we train a source-aware fault diagnosis model in each source domain and then use it to predict the fault labels of the target samples. Second, a similarity score between each source domain and target domain is computed based on their feature distributions using local discriminant analysis and Maximum Mean Discrepancy. Finally, the similarity scores are used as domain weights in a proposed classification decision fusion strategy that uses a weighted linear combination process of predicted fault labels to provide the final predicted labels for the target samples. The benefits of the adaptive weighting fusion based on the classification result level, which makes full use of the available data from multiple source domains, measures the differences in distribution between the source and target domains and automatically adjust the weights to improve the diagnostic capability of the target domain. The effectiveness of the proposed method for diagnosing bearing faults under different operating and measurement conditions was verified using a bearing data set provided by Case Western Reserve University.},
 year = {2022}
}

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TY  - JOUR
T1  - Classification Fusion for Bearing Fault Diagnosis with Multi-source Domain Shift
AU  - Huang Mu-sheng
AU  - Wu Song-song
AU  - Zheng Shi-yuan
AU  - Yu Xi
AU  - Zhuang Jia-yang
AU  - Jing Xiao-yuan
Y1  - 2022/05/12
PY  - 2022
N1  - https://doi.org/10.11648/j.acis.20221002.12
DO  - 10.11648/j.acis.20221002.12
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 17
EP  - 26
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20221002.12
AB  - Bearings are one of the most widely used components of rotating machinery, whose failure can cause serious injuries and economic losses, therefore bearing fault diagnosis is an essential step in maintaining the safe and stable operation of industrial processes. Bearing fault diagnosis aims to detect the bearing fault condition and accurately classify it into a fault category based on sensing signals, such as vibration data. In practical applications, bearings always work in different types of equipment and under various working conditions, leading to performance degradation of diagnosis models due to the domain gap between the training data and the test data. Domain adaptation has been developed to address the domain shift problem in bearing fault diagnosis with demonstrated efficacy. Current domain adaptation models focus on the single-source scenario, by ignoring that sensing data may be collected from multiple sources in practical applications, and then be annotated for mode training. In this situation, it is non-trivial to use the single-source domain adaptation model to address the multi-source domain shift problem, because the domain gap exists among the source domains and the target domain. To solve this problem, we propose a novel bearing fault diagnosis model based on classification decision fusion to address the problem of multi-source domain conversion. Firstly, we train a source-aware fault diagnosis model in each source domain and then use it to predict the fault labels of the target samples. Second, a similarity score between each source domain and target domain is computed based on their feature distributions using local discriminant analysis and Maximum Mean Discrepancy. Finally, the similarity scores are used as domain weights in a proposed classification decision fusion strategy that uses a weighted linear combination process of predicted fault labels to provide the final predicted labels for the target samples. The benefits of the adaptive weighting fusion based on the classification result level, which makes full use of the available data from multiple source domains, measures the differences in distribution between the source and target domains and automatically adjust the weights to improve the diagnostic capability of the target domain. The effectiveness of the proposed method for diagnosing bearing faults under different operating and measurement conditions was verified using a bearing data set provided by Case Western Reserve University.
VL  - 10
IS  - 2
ER  -

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Author Information

Huang Mu-sheng

School of Computer Science, Guangdong University of Petrochemical Technology, Maoming, China
Wu Song-song

School of Computer Science, Guangdong University of Petrochemical Technology, Maoming, China
Zheng Shi-yuan

School of Computer Science, Guangdong University of Petrochemical Technology, Maoming, China
Yu Xi

School of Computer Science, Guangdong University of Petrochemical Technology, Maoming, China
Zhuang Jia-yang

School of Computer Science, Guangdong University of Petrochemical Technology, Maoming, China
Jing Xiao-yuan

School of Computer Science, Guangdong University of Petrochemical Technology, Maoming, China

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Plain Text BibTeX RIS

APA Style

Huang Mu-sheng, Wu Song-song, Zheng Shi-yuan, Yu Xi, Zhuang Jia-yang, et al. (2022). Classification Fusion for Bearing Fault Diagnosis with Multi-source Domain Shift. Automation, Control and Intelligent Systems, 10(2), 17-26. https://doi.org/10.11648/j.acis.20221002.12

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ACS Style

Huang Mu-sheng; Wu Song-song; Zheng Shi-yuan; Yu Xi; Zhuang Jia-yang, et al. Classification Fusion for Bearing Fault Diagnosis with Multi-source Domain Shift. Autom. Control Intell. Syst. 2022, 10(2), 17-26. doi: 10.11648/j.acis.20221002.12

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AMA Style

Huang Mu-sheng, Wu Song-song, Zheng Shi-yuan, Yu Xi, Zhuang Jia-yang, et al. Classification Fusion for Bearing Fault Diagnosis with Multi-source Domain Shift. Autom Control Intell Syst. 2022;10(2):17-26. doi: 10.11648/j.acis.20221002.12

Copy | Download

@article{10.11648/j.acis.20221002.12,
  author = {Huang Mu-sheng and Wu Song-song and Zheng Shi-yuan and Yu Xi and Zhuang Jia-yang and Jing Xiao-yuan},
  title = {Classification Fusion for Bearing Fault Diagnosis with Multi-source Domain Shift},
  journal = {Automation, Control and Intelligent Systems},
  volume = {10},
  number = {2},
  pages = {17-26},
  doi = {10.11648/j.acis.20221002.12},
  url = {https://doi.org/10.11648/j.acis.20221002.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20221002.12},
  abstract = {Bearings are one of the most widely used components of rotating machinery, whose failure can cause serious injuries and economic losses, therefore bearing fault diagnosis is an essential step in maintaining the safe and stable operation of industrial processes. Bearing fault diagnosis aims to detect the bearing fault condition and accurately classify it into a fault category based on sensing signals, such as vibration data. In practical applications, bearings always work in different types of equipment and under various working conditions, leading to performance degradation of diagnosis models due to the domain gap between the training data and the test data. Domain adaptation has been developed to address the domain shift problem in bearing fault diagnosis with demonstrated efficacy. Current domain adaptation models focus on the single-source scenario, by ignoring that sensing data may be collected from multiple sources in practical applications, and then be annotated for mode training. In this situation, it is non-trivial to use the single-source domain adaptation model to address the multi-source domain shift problem, because the domain gap exists among the source domains and the target domain. To solve this problem, we propose a novel bearing fault diagnosis model based on classification decision fusion to address the problem of multi-source domain conversion. Firstly, we train a source-aware fault diagnosis model in each source domain and then use it to predict the fault labels of the target samples. Second, a similarity score between each source domain and target domain is computed based on their feature distributions using local discriminant analysis and Maximum Mean Discrepancy. Finally, the similarity scores are used as domain weights in a proposed classification decision fusion strategy that uses a weighted linear combination process of predicted fault labels to provide the final predicted labels for the target samples. The benefits of the adaptive weighting fusion based on the classification result level, which makes full use of the available data from multiple source domains, measures the differences in distribution between the source and target domains and automatically adjust the weights to improve the diagnostic capability of the target domain. The effectiveness of the proposed method for diagnosing bearing faults under different operating and measurement conditions was verified using a bearing data set provided by Case Western Reserve University.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Classification Fusion for Bearing Fault Diagnosis with Multi-source Domain Shift
AU  - Huang Mu-sheng
AU  - Wu Song-song
AU  - Zheng Shi-yuan
AU  - Yu Xi
AU  - Zhuang Jia-yang
AU  - Jing Xiao-yuan
Y1  - 2022/05/12
PY  - 2022
N1  - https://doi.org/10.11648/j.acis.20221002.12
DO  - 10.11648/j.acis.20221002.12
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 17
EP  - 26
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20221002.12
AB  - Bearings are one of the most widely used components of rotating machinery, whose failure can cause serious injuries and economic losses, therefore bearing fault diagnosis is an essential step in maintaining the safe and stable operation of industrial processes. Bearing fault diagnosis aims to detect the bearing fault condition and accurately classify it into a fault category based on sensing signals, such as vibration data. In practical applications, bearings always work in different types of equipment and under various working conditions, leading to performance degradation of diagnosis models due to the domain gap between the training data and the test data. Domain adaptation has been developed to address the domain shift problem in bearing fault diagnosis with demonstrated efficacy. Current domain adaptation models focus on the single-source scenario, by ignoring that sensing data may be collected from multiple sources in practical applications, and then be annotated for mode training. In this situation, it is non-trivial to use the single-source domain adaptation model to address the multi-source domain shift problem, because the domain gap exists among the source domains and the target domain. To solve this problem, we propose a novel bearing fault diagnosis model based on classification decision fusion to address the problem of multi-source domain conversion. Firstly, we train a source-aware fault diagnosis model in each source domain and then use it to predict the fault labels of the target samples. Second, a similarity score between each source domain and target domain is computed based on their feature distributions using local discriminant analysis and Maximum Mean Discrepancy. Finally, the similarity scores are used as domain weights in a proposed classification decision fusion strategy that uses a weighted linear combination process of predicted fault labels to provide the final predicted labels for the target samples. The benefits of the adaptive weighting fusion based on the classification result level, which makes full use of the available data from multiple source domains, measures the differences in distribution between the source and target domains and automatically adjust the weights to improve the diagnostic capability of the target domain. The effectiveness of the proposed method for diagnosing bearing faults under different operating and measurement conditions was verified using a bearing data set provided by Case Western Reserve University.
VL  - 10
IS  - 2
ER  -

Copy | Download