Online Knowledge Distillation Based Multiscale Threshold Denoising Networks for Fault Diagnosis of Transmission Systems

Xu, Y; Yan, X; Sun, B; Feng, K; Kou, L; Chen, Y; Li, Y; Chen, H; Tian, E; Ni, Q; Wang, Y

Online Knowledge Distillation Based Multiscale Threshold Denoising Networks for Fault Diagnosis of Transmission Systems

Xu, Y Yan, X Sun, B Feng, K Kou, L Chen, Y Li, Y Chen, H Tian, E Ni, Q Wang, Y

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Transportation Electrification, 2023, PP, (99), pp. 1-1
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Xu, Y
dc.contributor.author	Yan, X
dc.contributor.author	Sun, B
dc.contributor.author	Feng, K
dc.contributor.author	Kou, L
dc.contributor.author	Chen, Y
dc.contributor.author	Li, Y
dc.contributor.author	Chen, H
dc.contributor.author	Tian, E
dc.contributor.author	Ni, Q
dc.contributor.author	Wang, Y
dc.date.accessioned	2024-05-13T01:33:23Z
dc.date.available	2024-05-13T01:33:23Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Transportation Electrification, 2023, PP, (99), pp. 1-1
dc.identifier.issn	2332-7782
dc.identifier.issn	2332-7782
dc.identifier.uri	http://hdl.handle.net/10453/178859
dc.description.abstract	Convolutional neural networks (CNN) have developed rapidly in recent years, which has greatly promoted the advancement of intelligent fault diagnosis. Most currently available CNN-based diagnostic models are developed under the presumption that the acquired mechanical signals are invulnerable to noise. However, transmission systems usually operate under fluctuating conditions (e.g., variable speed and strong noise scenarios), making the fault-related pulse information in the mechanical signal easily swamped by noise. Therefore, it is challenging for these existing approaches to achieve satisfactory results in industrial scenarios. To deal with this problem, an online knowledge distillation-based multiscale threshold denoising network (OKD-MTDN) is developed in this research work. The main innovations and contributions of this research work include: 1) introducing a novel convolutional module, called the Multiscale Convolutional Module (MCM), alongside a Global Attention Module (GAM), for extracting a range of discriminative features generated from mechanical signals; 2) designing a multi-dilated threshold denoising module (MTDM) to expand the receptive field and filter out interference features; 3) establishing an online knowledge distillation (OKD) algorithm to improve the generalization capability of OKD-MTDN. The hF-MS planetary gearbox dataset and the real-running high-speed rail dataset is utilized to verify the effectiveness of the proposed method. Experimental results show that the developed OKD-MTDN can achieve satisfactory results in various nonstationary scenarios.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Transportation Electrification
dc.relation.isbasedon	10.1109/TTE.2023.3313986
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	4008 Electrical engineering
dc.title	Online Knowledge Distillation Based Multiscale Threshold Denoising Networks for Fault Diagnosis of Transmission Systems
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0906 Electrical and Electronic Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-05-13T01:33:17Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Convolutional neural networks (CNN) have developed rapidly in recent years, which has greatly promoted the advancement of intelligent fault diagnosis. Most currently available CNN-based diagnostic models are developed under the presumption that the acquired mechanical signals are invulnerable to noise. However, transmission systems usually operate under fluctuating conditions (e.g., variable speed and strong noise scenarios), making the fault-related pulse information in the mechanical signal easily swamped by noise. Therefore, it is challenging for these existing approaches to achieve satisfactory results in industrial scenarios. To deal with this problem, an online knowledge distillation-based multiscale threshold denoising network (OKD-MTDN) is developed in this research work. The main innovations and contributions of this research work include: 1) introducing a novel convolutional module, called the Multiscale Convolutional Module (MCM), alongside a Global Attention Module (GAM), for extracting a range of discriminative features generated from mechanical signals; 2) designing a multi-dilated threshold denoising module (MTDM) to expand the receptive field and filter out interference features; 3) establishing an online knowledge distillation (OKD) algorithm to improve the generalization capability of OKD-MTDN. The hF-MS planetary gearbox dataset and the real-running high-speed rail dataset is utilized to verify the effectiveness of the proposed method. Experimental results show that the developed OKD-MTDN can achieve satisfactory results in various nonstationary scenarios.

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http://hdl.handle.net/10453/178859