Fault Diagnosis of Bearings under Different Working Conditions based on MMD-GAN

Zhimin Li; Xianghua Wang; Rui Yang

doi:10.1109/CCDC52312.2021.9601507

Fault Diagnosis of Bearings under Different Working Conditions based on MMD-GAN

Zhimin Li, Xianghua Wang, Rui Yang

Department of Intelligent Science

Shandong University of Science and Technology

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

6 Citations (Scopus)

Abstract

In the actual work of rolling bearings, the probability distribution of output data will change due to changes in load and speed, which will lead to a decrease in the accuracy of the diagnostic model, or even failure. To solve this problem, this paper proposes a fault diagnosis model based on the combination of maximum mean discrepancy (MMD) and Generative adversarial network (GAN), which is called MMD-GAN. The proposed method extracts data features through a convolutional neural network, and then MMD and GAN are combined to reduce the distribution difference between the source and target domain dataset, result in more accurate fault diagnosis results. Finally, experiments were conducted through the CRWU rolling bearing data set, and the effectiveness of the proposed scheme has been verified.

Original language	English
Title of host publication	Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2906-2911
Number of pages	6
ISBN (Electronic)	9781665440899
DOIs	https://doi.org/10.1109/CCDC52312.2021.9601507
Publication status	Published - 2021
Event	33rd Chinese Control and Decision Conference, CCDC 2021 - Kunming, China Duration: 22 May 2021 → 24 May 2021

Publication series

Name	Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021

Conference

Conference	33rd Chinese Control and Decision Conference, CCDC 2021
Country/Territory	China
City	Kunming
Period	22/05/21 → 24/05/21

Keywords

Transfer learning
convolutional neural network
generative adversarial network
maximum mean discrepancy

Access to Document

10.1109/CCDC52312.2021.9601507

Cite this

Li, Z., Wang, X., & Yang, R. (2021). Fault Diagnosis of Bearings under Different Working Conditions based on MMD-GAN. In Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021 (pp. 2906-2911). (Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC52312.2021.9601507

@inproceedings{f3d10d40743946a9bd4966680d1962b5,

title = "Fault Diagnosis of Bearings under Different Working Conditions based on MMD-GAN",

abstract = "In the actual work of rolling bearings, the probability distribution of output data will change due to changes in load and speed, which will lead to a decrease in the accuracy of the diagnostic model, or even failure. To solve this problem, this paper proposes a fault diagnosis model based on the combination of maximum mean discrepancy (MMD) and Generative adversarial network (GAN), which is called MMD-GAN. The proposed method extracts data features through a convolutional neural network, and then MMD and GAN are combined to reduce the distribution difference between the source and target domain dataset, result in more accurate fault diagnosis results. Finally, experiments were conducted through the CRWU rolling bearing data set, and the effectiveness of the proposed scheme has been verified.",

keywords = "Transfer learning, convolutional neural network, generative adversarial network, maximum mean discrepancy",

author = "Zhimin Li and Xianghua Wang and Rui Yang",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 33rd Chinese Control and Decision Conference, CCDC 2021 ; Conference date: 22-05-2021 Through 24-05-2021",

year = "2021",

doi = "10.1109/CCDC52312.2021.9601507",

language = "English",

series = "Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2906--2911",

booktitle = "Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021",

}

Li, Z, Wang, X & Yang, R 2021, Fault Diagnosis of Bearings under Different Working Conditions based on MMD-GAN. in Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021. Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021, Institute of Electrical and Electronics Engineers Inc., pp. 2906-2911, 33rd Chinese Control and Decision Conference, CCDC 2021, Kunming, China, 22/05/21. https://doi.org/10.1109/CCDC52312.2021.9601507

Fault Diagnosis of Bearings under Different Working Conditions based on MMD-GAN. / Li, Zhimin; Wang, Xianghua; Yang, Rui.
Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 2906-2911 (Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Fault Diagnosis of Bearings under Different Working Conditions based on MMD-GAN

AU - Li, Zhimin

AU - Wang, Xianghua

AU - Yang, Rui

PY - 2021

Y1 - 2021

N2 - In the actual work of rolling bearings, the probability distribution of output data will change due to changes in load and speed, which will lead to a decrease in the accuracy of the diagnostic model, or even failure. To solve this problem, this paper proposes a fault diagnosis model based on the combination of maximum mean discrepancy (MMD) and Generative adversarial network (GAN), which is called MMD-GAN. The proposed method extracts data features through a convolutional neural network, and then MMD and GAN are combined to reduce the distribution difference between the source and target domain dataset, result in more accurate fault diagnosis results. Finally, experiments were conducted through the CRWU rolling bearing data set, and the effectiveness of the proposed scheme has been verified.

AB - In the actual work of rolling bearings, the probability distribution of output data will change due to changes in load and speed, which will lead to a decrease in the accuracy of the diagnostic model, or even failure. To solve this problem, this paper proposes a fault diagnosis model based on the combination of maximum mean discrepancy (MMD) and Generative adversarial network (GAN), which is called MMD-GAN. The proposed method extracts data features through a convolutional neural network, and then MMD and GAN are combined to reduce the distribution difference between the source and target domain dataset, result in more accurate fault diagnosis results. Finally, experiments were conducted through the CRWU rolling bearing data set, and the effectiveness of the proposed scheme has been verified.

KW - Transfer learning

KW - convolutional neural network

KW - generative adversarial network

KW - maximum mean discrepancy

UR - http://www.scopus.com/inward/record.url?scp=85125193850&partnerID=8YFLogxK

U2 - 10.1109/CCDC52312.2021.9601507

DO - 10.1109/CCDC52312.2021.9601507

M3 - Conference Proceeding

AN - SCOPUS:85125193850

T3 - Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021

SP - 2906

EP - 2911

BT - Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 33rd Chinese Control and Decision Conference, CCDC 2021

Y2 - 22 May 2021 through 24 May 2021

ER -

Li Z, Wang X, Yang R. Fault Diagnosis of Bearings under Different Working Conditions based on MMD-GAN. In Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 2906-2911. (Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021). doi: 10.1109/CCDC52312.2021.9601507

Fault Diagnosis of Bearings under Different Working Conditions based on MMD-GAN

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this