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Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks

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Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks. / Xia, Min; Li, Teng; Xu, Lin et al.
In: IEEE/ASME Transactions on Mechatronics, Vol. 23, No. 1, 01.02.2018, p. 101-110.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Xia, M, Li, T, Xu, L, Liu, L & De Silva, CW 2018, 'Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks', IEEE/ASME Transactions on Mechatronics, vol. 23, no. 1, pp. 101-110. https://doi.org/10.1109/TMECH.2017.2728371

APA

Xia, M., Li, T., Xu, L., Liu, L., & De Silva, C. W. (2018). Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks. IEEE/ASME Transactions on Mechatronics, 23(1), 101-110. https://doi.org/10.1109/TMECH.2017.2728371

Vancouver

Xia M, Li T, Xu L, Liu L, De Silva CW. Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks. IEEE/ASME Transactions on Mechatronics. 2018 Feb 1;23(1):101-110. Epub 2017 Jul 17. doi: 10.1109/TMECH.2017.2728371

Author

Xia, Min ; Li, Teng ; Xu, Lin et al. / Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks. In: IEEE/ASME Transactions on Mechatronics. 2018 ; Vol. 23, No. 1. pp. 101-110.

Bibtex

@article{519c3d89ea944fe9bdc547ff0695feb8,
title = "Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks",
abstract = "This paper presents a convolutional neural network (CNN) based approach for fault diagnosis of rotating machinery. The proposed approach incorporates sensor fusion by taking advantage of the CNN structure to achieve higher and more robust diagnosis accuracy. Both temporal and spatial information of the raw data from multiple sensors is considered during the training process of the CNN. Representative features can be extracted automatically from the raw signals. It avoids manual feature extraction or selection, which relies heavily on prior knowledge of specific machinery and fault types. The effectiveness of the developed method is evaluated by using datasets from two types of typical rotating machinery, roller bearings, and gearboxes. Compared with traditional approaches using manual feature extraction, the results show the superior diagnosis performance of the proposed method. The present approach can be extended to fault diagnosis of other machinery with various types of sensors due to its end to end feature learning capability.",
keywords = "Convolutional neural networks (CNNs), fault diagnosis, feature learning, rotating machinery, sensor fusion",
author = "Min Xia and Teng Li and Lin Xu and Lizhi Liu and {De Silva}, {Clarence W.}",
year = "2018",
month = feb,
day = "1",
doi = "10.1109/TMECH.2017.2728371",
language = "English",
volume = "23",
pages = "101--110",
journal = "IEEE/ASME Transactions on Mechatronics",
issn = "1083-4435",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "1",

}

RIS

TY - JOUR

T1 - Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks

AU - Xia, Min

AU - Li, Teng

AU - Xu, Lin

AU - Liu, Lizhi

AU - De Silva, Clarence W.

PY - 2018/2/1

Y1 - 2018/2/1

N2 - This paper presents a convolutional neural network (CNN) based approach for fault diagnosis of rotating machinery. The proposed approach incorporates sensor fusion by taking advantage of the CNN structure to achieve higher and more robust diagnosis accuracy. Both temporal and spatial information of the raw data from multiple sensors is considered during the training process of the CNN. Representative features can be extracted automatically from the raw signals. It avoids manual feature extraction or selection, which relies heavily on prior knowledge of specific machinery and fault types. The effectiveness of the developed method is evaluated by using datasets from two types of typical rotating machinery, roller bearings, and gearboxes. Compared with traditional approaches using manual feature extraction, the results show the superior diagnosis performance of the proposed method. The present approach can be extended to fault diagnosis of other machinery with various types of sensors due to its end to end feature learning capability.

AB - This paper presents a convolutional neural network (CNN) based approach for fault diagnosis of rotating machinery. The proposed approach incorporates sensor fusion by taking advantage of the CNN structure to achieve higher and more robust diagnosis accuracy. Both temporal and spatial information of the raw data from multiple sensors is considered during the training process of the CNN. Representative features can be extracted automatically from the raw signals. It avoids manual feature extraction or selection, which relies heavily on prior knowledge of specific machinery and fault types. The effectiveness of the developed method is evaluated by using datasets from two types of typical rotating machinery, roller bearings, and gearboxes. Compared with traditional approaches using manual feature extraction, the results show the superior diagnosis performance of the proposed method. The present approach can be extended to fault diagnosis of other machinery with various types of sensors due to its end to end feature learning capability.

KW - Convolutional neural networks (CNNs)

KW - fault diagnosis

KW - feature learning

KW - rotating machinery

KW - sensor fusion

U2 - 10.1109/TMECH.2017.2728371

DO - 10.1109/TMECH.2017.2728371

M3 - Journal article

AN - SCOPUS:85028941380

VL - 23

SP - 101

EP - 110

JO - IEEE/ASME Transactions on Mechatronics

JF - IEEE/ASME Transactions on Mechatronics

SN - 1083-4435

IS - 1

ER -