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  • Final-TIM-21-02506

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Fault diagnosis of a rotor-bearing system under variable rotating speeds using two-stage parameter transfer and infrared thermal images

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Fault diagnosis of a rotor-bearing system under variable rotating speeds using two-stage parameter transfer and infrared thermal images. / Shao, H.; Li, W.; Xia, M. et al.
In: IEEE Transactions on Instrumentation and Measurement, Vol. 70, 10.09.2021.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Shao, H, Li, W, Xia, M, Zhang, Y, Shen, C, Williams, D, Kennedy, A & De Silva, CW 2021, 'Fault diagnosis of a rotor-bearing system under variable rotating speeds using two-stage parameter transfer and infrared thermal images', IEEE Transactions on Instrumentation and Measurement, vol. 70. https://doi.org/10.1109/TIM.2021.3111977

APA

Shao, H., Li, W., Xia, M., Zhang, Y., Shen, C., Williams, D., Kennedy, A., & De Silva, C. W. (2021). Fault diagnosis of a rotor-bearing system under variable rotating speeds using two-stage parameter transfer and infrared thermal images. IEEE Transactions on Instrumentation and Measurement, 70. https://doi.org/10.1109/TIM.2021.3111977

Vancouver

Shao H, Li W, Xia M, Zhang Y, Shen C, Williams D et al. Fault diagnosis of a rotor-bearing system under variable rotating speeds using two-stage parameter transfer and infrared thermal images. IEEE Transactions on Instrumentation and Measurement. 2021 Sept 10;70. doi: 10.1109/TIM.2021.3111977

Author

Shao, H. ; Li, W. ; Xia, M. et al. / Fault diagnosis of a rotor-bearing system under variable rotating speeds using two-stage parameter transfer and infrared thermal images. In: IEEE Transactions on Instrumentation and Measurement. 2021 ; Vol. 70.

Bibtex

@article{887fbd935984448385fb60f8007b5f90,
title = "Fault diagnosis of a rotor-bearing system under variable rotating speeds using two-stage parameter transfer and infrared thermal images",
abstract = "Current fault diagnosis methods for rotor-bearing system are mostly based on analyzing the vibration signals collected at steady rotating speeds. In those methods, the data collected under one operating condition cannot be accurately used for diagnosis under a different condition. Moreover, in vibration monitoring, installing the necessary sensors will affect the equipment structure and hence the vibration response itself. The present paper proposes a new method based on two-stage parameter transfer and infrared thermal images for fault diagnosis of rotor-bearing system under variable rotating speeds. The method of parameter transfer enables the use of data (or parameters) acquired under one operating condition (called the source domain) to be extended for use in a different operating condition (called the target domain). First, scaled exponential linear unit (SELU) and modified stochastic gradient descent (MSGD) are used to construct an enhanced convolutional neural network (ECNN). Second, a stacked convolutional auto-encoder (CAE) trained based on unlabeled source-domain thermal images is employed to initialize a source-domain ECNN. Third, model parameters from the pre-trained source-domain ECNN are transferred to the target-domain ECNN to adapt to the characteristics of the target domain. The collected thermal images for a rotor-bearing system under variable speeds are used to test the transfer diagnosis performance of the proposed method. The experimental results demonstrate the performance improvement and the advantages of the proposed method. ",
keywords = "Fault diagnosis, Infrared thermal images, Rotor-bearing system, Two-stage parameter transfer, Variable rotating speeds, Convolution, Convolutional neural networks, Electric fault currents, Failure analysis, Fault detection, Gradient methods, Rotating machinery, Stochastic systems, Vibration analysis, Diagnosis performance, Different operating conditions, Fault diagnosis method, Infrared thermal image, Parameter transfers, Stochastic gradient descent, Vibration monitoring, Bearings (machine parts)",
author = "H. Shao and W. Li and M. Xia and Y. Zhang and C. Shen and Darren Williams and A. Kennedy and {De Silva}, C.W.",
note = "{\textcopyright}2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2021",
month = sep,
day = "10",
doi = "10.1109/TIM.2021.3111977",
language = "English",
volume = "70",
journal = "IEEE Transactions on Instrumentation and Measurement",
issn = "0018-9456",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",

}

RIS

TY - JOUR

T1 - Fault diagnosis of a rotor-bearing system under variable rotating speeds using two-stage parameter transfer and infrared thermal images

AU - Shao, H.

AU - Li, W.

AU - Xia, M.

AU - Zhang, Y.

AU - Shen, C.

AU - Williams, Darren

AU - Kennedy, A.

AU - De Silva, C.W.

N1 - ©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2021/9/10

Y1 - 2021/9/10

N2 - Current fault diagnosis methods for rotor-bearing system are mostly based on analyzing the vibration signals collected at steady rotating speeds. In those methods, the data collected under one operating condition cannot be accurately used for diagnosis under a different condition. Moreover, in vibration monitoring, installing the necessary sensors will affect the equipment structure and hence the vibration response itself. The present paper proposes a new method based on two-stage parameter transfer and infrared thermal images for fault diagnosis of rotor-bearing system under variable rotating speeds. The method of parameter transfer enables the use of data (or parameters) acquired under one operating condition (called the source domain) to be extended for use in a different operating condition (called the target domain). First, scaled exponential linear unit (SELU) and modified stochastic gradient descent (MSGD) are used to construct an enhanced convolutional neural network (ECNN). Second, a stacked convolutional auto-encoder (CAE) trained based on unlabeled source-domain thermal images is employed to initialize a source-domain ECNN. Third, model parameters from the pre-trained source-domain ECNN are transferred to the target-domain ECNN to adapt to the characteristics of the target domain. The collected thermal images for a rotor-bearing system under variable speeds are used to test the transfer diagnosis performance of the proposed method. The experimental results demonstrate the performance improvement and the advantages of the proposed method.

AB - Current fault diagnosis methods for rotor-bearing system are mostly based on analyzing the vibration signals collected at steady rotating speeds. In those methods, the data collected under one operating condition cannot be accurately used for diagnosis under a different condition. Moreover, in vibration monitoring, installing the necessary sensors will affect the equipment structure and hence the vibration response itself. The present paper proposes a new method based on two-stage parameter transfer and infrared thermal images for fault diagnosis of rotor-bearing system under variable rotating speeds. The method of parameter transfer enables the use of data (or parameters) acquired under one operating condition (called the source domain) to be extended for use in a different operating condition (called the target domain). First, scaled exponential linear unit (SELU) and modified stochastic gradient descent (MSGD) are used to construct an enhanced convolutional neural network (ECNN). Second, a stacked convolutional auto-encoder (CAE) trained based on unlabeled source-domain thermal images is employed to initialize a source-domain ECNN. Third, model parameters from the pre-trained source-domain ECNN are transferred to the target-domain ECNN to adapt to the characteristics of the target domain. The collected thermal images for a rotor-bearing system under variable speeds are used to test the transfer diagnosis performance of the proposed method. The experimental results demonstrate the performance improvement and the advantages of the proposed method.

KW - Fault diagnosis

KW - Infrared thermal images

KW - Rotor-bearing system

KW - Two-stage parameter transfer

KW - Variable rotating speeds

KW - Convolution

KW - Convolutional neural networks

KW - Electric fault currents

KW - Failure analysis

KW - Fault detection

KW - Gradient methods

KW - Rotating machinery

KW - Stochastic systems

KW - Vibration analysis

KW - Diagnosis performance

KW - Different operating conditions

KW - Fault diagnosis method

KW - Infrared thermal image

KW - Parameter transfers

KW - Stochastic gradient descent

KW - Vibration monitoring

KW - Bearings (machine parts)

U2 - 10.1109/TIM.2021.3111977

DO - 10.1109/TIM.2021.3111977

M3 - Journal article

VL - 70

JO - IEEE Transactions on Instrumentation and Measurement

JF - IEEE Transactions on Instrumentation and Measurement

SN - 0018-9456

ER -