Fault Diagnosis for Rotating Machinery Gearbox based on 1DCNN-RF

Zhimin Li; Qi Han; Rui Yang; Xianghua Wang; Mengjie Huang

doi:10.1109/ISCID51228.2020.00091

Fault Diagnosis for Rotating Machinery Gearbox based on 1DCNN-RF

Zhimin Li, Qi Han, Rui Yang^*, Xianghua Wang, Mengjie Huang

^*Corresponding author for this work

Shandong University of Science and Technology

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

8 Citations (Scopus)

Abstract

In this paper, a fault diagnosis method combining one-dimensional convolutional neural network (1DCNN) and random forest (RF), which is called 1DCNN-RF, is proposed for rotating machinery gearbox. This method uses 1DCNN to extract features from the collected multiple sensor signals, and then uses RF algorithm for classification. Compared to the existing approaches, this algorithm can improve the accuracy of fault diagnosis for rotating machinery gearbox. Finally, experiments are conducted on the Wind Tfirbine Drivetrain Diagnostic Simulator (WTDDS) to show the effectiveness of the proposed scheme.

Original language	English
Title of host publication	Proceedings - 2020 13th International Symposium on Computational Intelligence and Design, ISCID 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	376-379
Number of pages	4
ISBN (Electronic)	9781728184463
DOIs	https://doi.org/10.1109/ISCID51228.2020.00091
Publication status	Published - Dec 2020
Event	13th International Symposium on Computational Intelligence and Design, ISCID 2020 - Hangzhou, China Duration: 12 Dec 2020 → 13 Dec 2020

Publication series

Name	Proceedings - 2020 13th International Symposium on Computational Intelligence and Design, ISCID 2020

Conference

Conference	13th International Symposium on Computational Intelligence and Design, ISCID 2020
Country/Territory	China
City	Hangzhou
Period	12/12/20 → 13/12/20

Keywords

Fault diagnosis
Feature extraction
One-dimensional convolutional neural network (1DCNN)
Random forest (RF)
ratating machinery

Access to Document

10.1109/ISCID51228.2020.00091

Cite this

Li, Z., Han, Q., Yang, R., Wang, X., & Huang, M. (2020). Fault Diagnosis for Rotating Machinery Gearbox based on 1DCNN-RF. In Proceedings - 2020 13th International Symposium on Computational Intelligence and Design, ISCID 2020 (pp. 376-379). Article 9325753 (Proceedings - 2020 13th International Symposium on Computational Intelligence and Design, ISCID 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCID51228.2020.00091

Li, Zhimin ; Han, Qi ; Yang, Rui et al. / Fault Diagnosis for Rotating Machinery Gearbox based on 1DCNN-RF. Proceedings - 2020 13th International Symposium on Computational Intelligence and Design, ISCID 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 376-379 (Proceedings - 2020 13th International Symposium on Computational Intelligence and Design, ISCID 2020).

@inproceedings{771812daaa9d45929ade5a6eb4463a37,

title = "Fault Diagnosis for Rotating Machinery Gearbox based on 1DCNN-RF",

abstract = "In this paper, a fault diagnosis method combining one-dimensional convolutional neural network (1DCNN) and random forest (RF), which is called 1DCNN-RF, is proposed for rotating machinery gearbox. This method uses 1DCNN to extract features from the collected multiple sensor signals, and then uses RF algorithm for classification. Compared to the existing approaches, this algorithm can improve the accuracy of fault diagnosis for rotating machinery gearbox. Finally, experiments are conducted on the Wind Tfirbine Drivetrain Diagnostic Simulator (WTDDS) to show the effectiveness of the proposed scheme.",

keywords = "Fault diagnosis, Feature extraction, One-dimensional convolutional neural network (1DCNN), Random forest (RF), ratating machinery",

author = "Zhimin Li and Qi Han and Rui Yang and Xianghua Wang and Mengjie Huang",

note = "Publisher Copyright: {\textcopyright}2020 IEEE; 13th International Symposium on Computational Intelligence and Design, ISCID 2020 ; Conference date: 12-12-2020 Through 13-12-2020",

year = "2020",

month = dec,

doi = "10.1109/ISCID51228.2020.00091",

language = "English",

series = "Proceedings - 2020 13th International Symposium on Computational Intelligence and Design, ISCID 2020",

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

pages = "376--379",

booktitle = "Proceedings - 2020 13th International Symposium on Computational Intelligence and Design, ISCID 2020",

}

Li, Z, Han, Q, Yang, R, Wang, X & Huang, M 2020, Fault Diagnosis for Rotating Machinery Gearbox based on 1DCNN-RF. in Proceedings - 2020 13th International Symposium on Computational Intelligence and Design, ISCID 2020., 9325753, Proceedings - 2020 13th International Symposium on Computational Intelligence and Design, ISCID 2020, Institute of Electrical and Electronics Engineers Inc., pp. 376-379, 13th International Symposium on Computational Intelligence and Design, ISCID 2020, Hangzhou, China, 12/12/20. https://doi.org/10.1109/ISCID51228.2020.00091

Fault Diagnosis for Rotating Machinery Gearbox based on 1DCNN-RF. / Li, Zhimin; Han, Qi; Yang, Rui et al.
Proceedings - 2020 13th International Symposium on Computational Intelligence and Design, ISCID 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 376-379 9325753 (Proceedings - 2020 13th International Symposium on Computational Intelligence and Design, ISCID 2020).

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

TY - GEN

T1 - Fault Diagnosis for Rotating Machinery Gearbox based on 1DCNN-RF

AU - Li, Zhimin

AU - Han, Qi

AU - Yang, Rui

AU - Wang, Xianghua

AU - Huang, Mengjie

PY - 2020/12

Y1 - 2020/12

N2 - In this paper, a fault diagnosis method combining one-dimensional convolutional neural network (1DCNN) and random forest (RF), which is called 1DCNN-RF, is proposed for rotating machinery gearbox. This method uses 1DCNN to extract features from the collected multiple sensor signals, and then uses RF algorithm for classification. Compared to the existing approaches, this algorithm can improve the accuracy of fault diagnosis for rotating machinery gearbox. Finally, experiments are conducted on the Wind Tfirbine Drivetrain Diagnostic Simulator (WTDDS) to show the effectiveness of the proposed scheme.

AB - In this paper, a fault diagnosis method combining one-dimensional convolutional neural network (1DCNN) and random forest (RF), which is called 1DCNN-RF, is proposed for rotating machinery gearbox. This method uses 1DCNN to extract features from the collected multiple sensor signals, and then uses RF algorithm for classification. Compared to the existing approaches, this algorithm can improve the accuracy of fault diagnosis for rotating machinery gearbox. Finally, experiments are conducted on the Wind Tfirbine Drivetrain Diagnostic Simulator (WTDDS) to show the effectiveness of the proposed scheme.

KW - Fault diagnosis

KW - Feature extraction

KW - One-dimensional convolutional neural network (1DCNN)

KW - Random forest (RF)

KW - ratating machinery

UR - http://www.scopus.com/inward/record.url?scp=85100434563&partnerID=8YFLogxK

U2 - 10.1109/ISCID51228.2020.00091

DO - 10.1109/ISCID51228.2020.00091

M3 - Conference Proceeding

AN - SCOPUS:85100434563

T3 - Proceedings - 2020 13th International Symposium on Computational Intelligence and Design, ISCID 2020

SP - 376

EP - 379

BT - Proceedings - 2020 13th International Symposium on Computational Intelligence and Design, ISCID 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 13th International Symposium on Computational Intelligence and Design, ISCID 2020

Y2 - 12 December 2020 through 13 December 2020

ER -

Li Z, Han Q, Yang R, Wang X, Huang M. Fault Diagnosis for Rotating Machinery Gearbox based on 1DCNN-RF. In Proceedings - 2020 13th International Symposium on Computational Intelligence and Design, ISCID 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 376-379. 9325753. (Proceedings - 2020 13th International Symposium on Computational Intelligence and Design, ISCID 2020). doi: 10.1109/ISCID51228.2020.00091

Fault Diagnosis for Rotating Machinery Gearbox based on 1DCNN-RF

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Cite this