Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing

Fault diagnosis of rolling bearings has attracted extensive attention in industrial fields, which plays a vital role in guaranteeing the reliability, safety, and economical efficiency of mechanical systems. Traditional data-driven fault diagnosis methods require obtaining a dataset of full failure m...

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Bibliographic Details
Published in:Reliability engineering & system safety Vol. 234; p. 109186
Main Authors: Zhang, Yongchao, Ji, J.C., Ren, Zhaohui, Ni, Qing, Gu, Fengshou, Feng, Ke, Yu, Kun, Ge, Jian, Lei, Zihao, Liu, Zheng
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.06.2023
Subjects:
Digital twin
Domain adaptation
Fault diagnosis
Rolling bearing
Transformer
Fault diagnosis
Domain adaptation
Transformer
Digital twin
Rolling bearing
ISSN:0951-8320, 1879-0836
Online Access:Get full text
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Summary:Fault diagnosis of rolling bearings has attracted extensive attention in industrial fields, which plays a vital role in guaranteeing the reliability, safety, and economical efficiency of mechanical systems. Traditional data-driven fault diagnosis methods require obtaining a dataset of full failure modes in advance as the training data. However, this kind of dataset is not always available in some critical industrial scenarios, which impairs the practicability of the data-driven fault diagnosis methods for various applications. A digital twin, which establishes a virtual representation of a physical entity to mirror its operating conditions, would make fault diagnosis of rolling bearings feasible when the fault data are insufficient. In this paper, we propose a novel digital twin-driven approach for implementing fault diagnosis of rolling bearings with insufficient training data. First, a dynamics-based virtual representation of rolling bearings is built to generate simulated data. Then, a Transformer-based network is developed to learn the knowledge of the simulated data for diagnostics. Meanwhile, a selective adversarial strategy is introduced to achieve cross-domain feature alignments in scenarios where the health conditions of the measured data are unknown. To this end, this study proposes a digital twin-driven fault diagnosis framework by using labeled simulated data and unlabeled measured data. The experimental results show that the proposed method can obtain high diagnostic performance when the real-world data is unlabeled and has unknown health conditions, proving that the proposed method has significant benefits for the health management of critical rolling bearings. •A digital twin-driven intelligent diagnosis method is developed.•A high-fidelity digital twin model is built for the rolling bearing.•A partial domain adaptation algorithm is introduced for bearing condition assessment.•One test is conducted to validate the effectiveness of the proposed methodology.
ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2023.109186