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Intelligent fault diagnosis of rotor-bearing system under varying working conditions with modified transfer CNN and thermal images

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Intelligent fault diagnosis of rotor-bearing system under varying working conditions with modified transfer CNN and thermal images. / Shao, H.; Xia, M.; Han, G.; Zhang, Y.; Wan, J.

In: IEEE Transactions on Industrial Informatics, 30.06.2020.

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Shao, H. ; Xia, M. ; Han, G. ; Zhang, Y. ; Wan, J. / Intelligent fault diagnosis of rotor-bearing system under varying working conditions with modified transfer CNN and thermal images. In: IEEE Transactions on Industrial Informatics. 2020.

Bibtex

@article{efcc0ced6a594144afc59e13423951ca,
title = "Intelligent fault diagnosis of rotor-bearing system under varying working conditions with modified transfer CNN and thermal images",
abstract = "The existing intelligent fault diagnosis methods of rotor-bearing system mainly focus on vibration analysis under steady operation, which has low adaptability to new scenes. In this paper, a new framework for rotor-bearing system fault diagnosis under varying working conditions is proposed by using a modified convolutional neural network (CNN) with transfer learning. First, infrared thermal images are collected and used to characterize the health condition of rotor-bearing system. Second, modified CNN is developed by introducing stochastic pooling and Leaky rectified linear unit to overcome the training problems in classical CNN. Finally, parameter transfer is used to enable the source modified CNN to adapt to the target domain, which solves the problem of limited available training data in the target domain. The proposed method is applied to analyze thermal images of rotor-bearing system collected under different working conditions. The results show that the proposed method outperforms other cutting edge methods in fault diagnosis of rotor-bearing system.",
author = "H. Shao and M. Xia and G. Han and Y. Zhang and J. Wan",
year = "2020",
month = jun,
day = "30",
doi = "10.1109/TII.2020.3005965",
language = "English",
journal = "IEEE Transactions on Industrial Informatics",
issn = "1551-3203",
publisher = "IEEE Computer Society",

}

RIS

TY - JOUR

T1 - Intelligent fault diagnosis of rotor-bearing system under varying working conditions with modified transfer CNN and thermal images

AU - Shao, H.

AU - Xia, M.

AU - Han, G.

AU - Zhang, Y.

AU - Wan, J.

PY - 2020/6/30

Y1 - 2020/6/30

N2 - The existing intelligent fault diagnosis methods of rotor-bearing system mainly focus on vibration analysis under steady operation, which has low adaptability to new scenes. In this paper, a new framework for rotor-bearing system fault diagnosis under varying working conditions is proposed by using a modified convolutional neural network (CNN) with transfer learning. First, infrared thermal images are collected and used to characterize the health condition of rotor-bearing system. Second, modified CNN is developed by introducing stochastic pooling and Leaky rectified linear unit to overcome the training problems in classical CNN. Finally, parameter transfer is used to enable the source modified CNN to adapt to the target domain, which solves the problem of limited available training data in the target domain. The proposed method is applied to analyze thermal images of rotor-bearing system collected under different working conditions. The results show that the proposed method outperforms other cutting edge methods in fault diagnosis of rotor-bearing system.

AB - The existing intelligent fault diagnosis methods of rotor-bearing system mainly focus on vibration analysis under steady operation, which has low adaptability to new scenes. In this paper, a new framework for rotor-bearing system fault diagnosis under varying working conditions is proposed by using a modified convolutional neural network (CNN) with transfer learning. First, infrared thermal images are collected and used to characterize the health condition of rotor-bearing system. Second, modified CNN is developed by introducing stochastic pooling and Leaky rectified linear unit to overcome the training problems in classical CNN. Finally, parameter transfer is used to enable the source modified CNN to adapt to the target domain, which solves the problem of limited available training data in the target domain. The proposed method is applied to analyze thermal images of rotor-bearing system collected under different working conditions. The results show that the proposed method outperforms other cutting edge methods in fault diagnosis of rotor-bearing system.

U2 - 10.1109/TII.2020.3005965

DO - 10.1109/TII.2020.3005965

M3 - Journal article

JO - IEEE Transactions on Industrial Informatics

JF - IEEE Transactions on Industrial Informatics

SN - 1551-3203

ER -