Digital twin-assisted dual transfer: a novel information-model adaptation method for rolling bearing fault diagnosis

Li, Zixian; Ding, Xiaoxi; Song, Zhenzhen; Wang, Liming; Qin, Bo; Huang, Wenbin

doi:10.1016/j.inffus.2024.102271

Information Fusion

2024

DOI: 10.1016/j.inffus.2024.102271

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Digital twin-assisted dual transfer: a novel information-model adaptation method for rolling bearing fault diagnosis

Zixian Li,

Xiaoxi Ding,

Zhenzhen Song

et al.

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Cited by 13 publications

References 48 publications

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Diagnosis of incipient faults in wind turbine bearings based on ICEEMDAN–IMCKD

Li,

Han

2024

Int Journal of Mech Sys Dyn

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To address the difficulty in extracting early fault feature signals of rolling bearings, this paper proposes a novel weak fault diagnosis method for rolling bearings. This method combines the Improved Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and the Improved Maximum Correlated Kurtosis Deconvolution (IMCKD). Utilizing the kurtosis criterion, the intrinsic mode functions obtained through ICEEMDAN are reconstructed and denoised using IMCKD, which significantly reduces noise in the measured signal. This approach maximizes the energy amplitude at the fault characteristic frequency, facilitating fault feature identification. Experimental studies on two test benches demonstrate that this method effectively reduces noise interference and highlights the fault frequency components. Compared with traditional methods, it significantly improves the signal‐to‐noise ratio and more accurately identifies fault features, meeting the requirements for discriminating rolling bearing faults. The method proposed in this study was applied to the measured vibration signals of the gearbox bearings in the new high‐speed wire department of a Long Products Mill. It successfully extracted weak characteristic information of early bearing faults, achieving the expected diagnostic results. This further validates the effectiveness of the ICEEMDAN–IMCKD method in practical engineering applications, demonstrating significant engineering value for detecting and extracting weak impact characteristics in rolling bearings.

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Diagnosis of incipient faults in wind turbine bearings based on ICEEMDAN–IMCKD

Li,

Han

2024

Int Journal of Mech Sys Dyn

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Digital twin-assisted interpretable transfer learning: A novel wavelet-based framework for intelligent fault diagnostics from simulated domain to real industrial domain

Li,

Jiang,

et al. 2024

Advanced Engineering Informatics

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Physics-informed probabilistic deep network with interpretable mechanism for trustworthy mechanical fault diagnosis

Xu,

Zhao,

Wang

et al. 2024

Advanced Engineering Informatics

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Digital twin-assisted dual transfer: a novel information-model adaptation method for rolling bearing fault diagnosis

Cited by 13 publications

References 48 publications

Diagnosis of incipient faults in wind turbine bearings based on ICEEMDAN–IMCKD

Diagnosis of incipient faults in wind turbine bearings based on ICEEMDAN–IMCKD

Digital twin-assisted interpretable transfer learning: A novel wavelet-based framework for intelligent fault diagnostics from simulated domain to real industrial domain

Physics-informed probabilistic deep network with interpretable mechanism for trustworthy mechanical fault diagnosis

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