A multi-modal gearbox fault diagnosis method based on deep transfer learning

By constructing a multimodal adversarial transfer network and utilizing deep transfer learning, the problem of fault diagnosis for unlabeled gearbox samples was solved, achieving high-accuracy diagnosis under different operating conditions and fault types, thus improving the fault diagnosis capability of gearboxes.

CN115600150BActive Publication Date: 2026-05-01ZHENGZHOU UNIV
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Patent Information

Application Number
CN202211174296.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-09-26
Publication Date
2026-05-01
Estimated Expiration
2042-09-26

AI Technical Summary

Technical Problem

In practical engineering, the fault diagnosis capability of gearboxes with unlabeled samples is poor, especially when there are many normal data samples and vibration data are not labeled, existing technologies are difficult to effectively diagnose faults.

Method used

A multimodal gearbox fault diagnosis method based on deep transfer learning is adopted. Through multimodal information fusion and transfer learning, a multimodal adversarial transfer network is constructed. Features are extracted using a one-dimensional convolutional neural network, and the network parameters are optimized through adversarial training to achieve unsupervised fault diagnosis.

Benefits of technology

It effectively solves the problem of gearbox fault diagnosis under unlabeled samples, improves diagnostic accuracy and generalization performance, and can achieve effective cross-domain diagnosis under different operating conditions and fault types.

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Abstract

This invention discloses a multimodal gearbox fault diagnosis method based on deep transfer learning, addressing the problem of poor gearbox fault diagnosis capability under unlabeled sample conditions, belonging to the field of gearbox fault diagnosis technology. The method includes: collecting raw vibration signals under different operating conditions, defining them as source and target domains; fusing multimodal information in the time and frequency domains using a data-level fusion method and dividing the collected signals into samples; constructing a deep multimodal adversarial transfer network model, extracting fault information features from the source and target domains through iterative adversarial training to adapt to the joint probability distribution of the source and target domains, and utilizing the abundant fault label information in the source domain to ensure accurate fault category discrimination; finally, obtaining a trained transfer diagnosis model for the target domain. This method is applicable to gearbox fault diagnosis under different operating conditions, i.e., transfer diagnosis between different operating conditions or different fault types, exhibiting high accuracy and good generalization performance.
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