Bearing Fault Diagnosis Based on Optimized Deep Hybrid Kernel Extreme Learning Machine

Zhenyu Qi; Liling Ma; Junzheng Wang; Shanhao Feng

doi:10.1109/CCDC58219.2023.10326628

Bearing Fault Diagnosis Based on Optimized Deep Hybrid Kernel Extreme Learning Machine

Zhenyu Qi^*, Liling Ma, Junzheng Wang, Shanhao Feng

^*Corresponding author for this work

School of Automation

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

1 Citation (Scopus)

Abstract

Bearings are important components in mechanical equipment. Fault diagnosis of bearings is of great significance. High accuracy and strong adaptability are necessary for a bearing fault diagnosis method. In this paper, a fault diagnosis method based on an optimized deep hybrid kernel extreme learning machine is proposed. This method adds the idea of deep learning to the traditional machine learning method, and has the characteristics of simple implementation and strong feature extraction ability. In addition, the sparrow search optimization algorithm is used to optimize the parameters of the diagnostic model, so that the model can achieve the best effectiveness. Experiments show that our proposed method can achieve satisfying performance on the same working condition, different working conditions and imbalanced datasets.

Original language	English
Title of host publication	Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3033-3038
Number of pages	6
ISBN (Electronic)	9798350334722
DOIs	https://doi.org/10.1109/CCDC58219.2023.10326628
Publication status	Published - 2023
Event	35th Chinese Control and Decision Conference, CCDC 2023 - Yichang, China Duration: 20 May 2023 → 22 May 2023

Publication series

Name	Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023

Conference

Conference	35th Chinese Control and Decision Conference, CCDC 2023
Country/Territory	China
City	Yichang
Period	20/05/23 → 22/05/23

Keywords

bearing fault diagnosis
deep learning
hybrid kernel extreme learning machine
sparrow search algorithm

Access to Document

10.1109/CCDC58219.2023.10326628

Cite this

Qi, Z., Ma, L., Wang, J., & Feng, S. (2023). Bearing Fault Diagnosis Based on Optimized Deep Hybrid Kernel Extreme Learning Machine. In Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023 (pp. 3033-3038). (Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC58219.2023.10326628

@inproceedings{539d70a3021b4f9aab427aef6f803a13,

title = "Bearing Fault Diagnosis Based on Optimized Deep Hybrid Kernel Extreme Learning Machine",

abstract = "Bearings are important components in mechanical equipment. Fault diagnosis of bearings is of great significance. High accuracy and strong adaptability are necessary for a bearing fault diagnosis method. In this paper, a fault diagnosis method based on an optimized deep hybrid kernel extreme learning machine is proposed. This method adds the idea of deep learning to the traditional machine learning method, and has the characteristics of simple implementation and strong feature extraction ability. In addition, the sparrow search optimization algorithm is used to optimize the parameters of the diagnostic model, so that the model can achieve the best effectiveness. Experiments show that our proposed method can achieve satisfying performance on the same working condition, different working conditions and imbalanced datasets.",

keywords = "bearing fault diagnosis, deep learning, hybrid kernel extreme learning machine, sparrow search algorithm",

author = "Zhenyu Qi and Liling Ma and Junzheng Wang and Shanhao Feng",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 35th Chinese Control and Decision Conference, CCDC 2023 ; Conference date: 20-05-2023 Through 22-05-2023",

year = "2023",

doi = "10.1109/CCDC58219.2023.10326628",

language = "English",

series = "Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023",

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

pages = "3033--3038",

booktitle = "Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023",

address = "United States",

}

Qi, Z, Ma, L , Wang, J & Feng, S 2023, Bearing Fault Diagnosis Based on Optimized Deep Hybrid Kernel Extreme Learning Machine. in Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023. Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023, Institute of Electrical and Electronics Engineers Inc., pp. 3033-3038, 35th Chinese Control and Decision Conference, CCDC 2023, Yichang, China, 20/05/23. https://doi.org/10.1109/CCDC58219.2023.10326628

Bearing Fault Diagnosis Based on Optimized Deep Hybrid Kernel Extreme Learning Machine. / Qi, Zhenyu; Ma, Liling ; Wang, Junzheng et al.
Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 3033-3038 (Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023).

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

TY - GEN

T1 - Bearing Fault Diagnosis Based on Optimized Deep Hybrid Kernel Extreme Learning Machine

AU - Qi, Zhenyu

AU - Ma, Liling

AU - Wang, Junzheng

AU - Feng, Shanhao

PY - 2023

Y1 - 2023

N2 - Bearings are important components in mechanical equipment. Fault diagnosis of bearings is of great significance. High accuracy and strong adaptability are necessary for a bearing fault diagnosis method. In this paper, a fault diagnosis method based on an optimized deep hybrid kernel extreme learning machine is proposed. This method adds the idea of deep learning to the traditional machine learning method, and has the characteristics of simple implementation and strong feature extraction ability. In addition, the sparrow search optimization algorithm is used to optimize the parameters of the diagnostic model, so that the model can achieve the best effectiveness. Experiments show that our proposed method can achieve satisfying performance on the same working condition, different working conditions and imbalanced datasets.

AB - Bearings are important components in mechanical equipment. Fault diagnosis of bearings is of great significance. High accuracy and strong adaptability are necessary for a bearing fault diagnosis method. In this paper, a fault diagnosis method based on an optimized deep hybrid kernel extreme learning machine is proposed. This method adds the idea of deep learning to the traditional machine learning method, and has the characteristics of simple implementation and strong feature extraction ability. In addition, the sparrow search optimization algorithm is used to optimize the parameters of the diagnostic model, so that the model can achieve the best effectiveness. Experiments show that our proposed method can achieve satisfying performance on the same working condition, different working conditions and imbalanced datasets.

KW - bearing fault diagnosis

KW - deep learning

KW - hybrid kernel extreme learning machine

KW - sparrow search algorithm

UR - http://www.scopus.com/inward/record.url?scp=85181817583&partnerID=8YFLogxK

U2 - 10.1109/CCDC58219.2023.10326628

DO - 10.1109/CCDC58219.2023.10326628

M3 - Conference contribution

AN - SCOPUS:85181817583

T3 - Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023

SP - 3033

EP - 3038

BT - Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 35th Chinese Control and Decision Conference, CCDC 2023

Y2 - 20 May 2023 through 22 May 2023

ER -

Qi Z, Ma L , Wang J, Feng S. Bearing Fault Diagnosis Based on Optimized Deep Hybrid Kernel Extreme Learning Machine. In Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 3033-3038. (Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023). doi: 10.1109/CCDC58219.2023.10326628

Bearing Fault Diagnosis Based on Optimized Deep Hybrid Kernel Extreme Learning Machine

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