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

Shao, H. and Li, W. and Xia, M. and Zhang, Y. and Shen, C. and Williams, Darren and Kennedy, A. and 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. ISSN 0018-9456

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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.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Instrumentation and Measurement
Additional Information:
©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.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/3100/3105
Subjects:
?? fault diagnosisinfrared thermal imagesrotor-bearing systemtwo-stage parameter transfervariable rotating speedsconvolutionconvolutional neural networkselectric fault currentsfailure analysisfault detectiongradient methodsrotating machinerystochastic system ??
ID Code:
160544
Deposited By:
Deposited On:
05 Oct 2021 09:25
Refereed?:
Yes
Published?:
Published
Last Modified:
05 Sep 2024 00:56