Data-Driven Prognostic Scheme for Bearings Based on a Novel Health Indicator and Gated Recurrent Unit Network

Ni, Q; Ji, JC; Feng, K

Data-Driven Prognostic Scheme for Bearings Based on a Novel Health Indicator and Gated Recurrent Unit Network

Ni, Q Ji, JC Feng, K

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Industrial Informatics, 2023, 19, (2), pp. 1301-1311
Issue Date:: 2023-02-01

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Field	Value	Language
dc.contributor.author	Ni, Q
dc.contributor.author	Ji, JC
dc.contributor.author	Feng, K
dc.date.accessioned	2023-11-07T01:54:19Z
dc.date.available	2023-11-07T01:54:19Z
dc.date.issued	2023-02-01
dc.identifier.citation	IEEE Transactions on Industrial Informatics, 2023, 19, (2), pp. 1301-1311
dc.identifier.issn	1551-3203
dc.identifier.issn	1941-0050
dc.identifier.uri	http://hdl.handle.net/10453/173127
dc.description.abstract	The prognosis of bearings is vital for condition-based maintenance of rotating machinery. This article proposes a systematic prognostic scheme for rolling element bearings. The proposed scheme infers the degradation progression by developing a novel health indicator (HI). This novel HI, derived from the spectral correlation, Wasserstein distance, and linear rectification, can reflect the changes in the probability distribution of all cyclic power-spectra over time. In other words, any form of variation in modulation characteristics can be revealed through the proposed novel indicator, even for the weak information buried by the internal or external noise. Furthermore, the developed HI can eliminate random fluctuations that often impair the remaining useful life (RUL) prediction accuracy. Then, a 3 ${\boldsymbol{\sigma }}$ criterion-based technique is introduced to divide health stages. After that, the gated recurrent unit network is employed to predict the RUL of the bearing system, integrated with the Bayesian optimization algorithm to tune the optimal hyperparameters adaptively. This renders the establishment of an intelligent prognosis model with high prediction accuracy and generalization ability. Finally, experimental validations are conducted using the run-to-failure datasets of bearings. The obtained results demonstrate that the proposed HI has better monotonicity, and the proposed prognostic scheme can predict the RUL with high accuracy.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Industrial Informatics
dc.relation.isbasedon	10.1109/TII.2022.3169465
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.subject.classification	Electrical & Electronic Engineering
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Data-Driven Prognostic Scheme for Bearings Based on a Novel Health Indicator and Gated Recurrent Unit Network
dc.type	Journal Article
utslib.citation.volume	19
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-11-07T01:54:18Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	19
utslib.citation.issue	2

Abstract:

The prognosis of bearings is vital for condition-based maintenance of rotating machinery. This article proposes a systematic prognostic scheme for rolling element bearings. The proposed scheme infers the degradation progression by developing a novel health indicator (HI). This novel HI, derived from the spectral correlation, Wasserstein distance, and linear rectification, can reflect the changes in the probability distribution of all cyclic power-spectra over time. In other words, any form of variation in modulation characteristics can be revealed through the proposed novel indicator, even for the weak information buried by the internal or external noise. Furthermore, the developed HI can eliminate random fluctuations that often impair the remaining useful life (RUL) prediction accuracy. Then, a 3 ${\boldsymbol{\sigma }}$ criterion-based technique is introduced to divide health stages. After that, the gated recurrent unit network is employed to predict the RUL of the bearing system, integrated with the Bayesian optimization algorithm to tune the optimal hyperparameters adaptively. This renders the establishment of an intelligent prognosis model with high prediction accuracy and generalization ability. Finally, experimental validations are conducted using the run-to-failure datasets of bearings. The obtained results demonstrate that the proposed HI has better monotonicity, and the proposed prognostic scheme can predict the RUL with high accuracy.

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http://hdl.handle.net/10453/173127