Fault diagnosis of cross-condition gearbox based on IDANN

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Journal of Vibration and Shock ›› 2025, Vol. 44 ›› Issue (9) : 282-289.

FAULT DIAGNOSIS ANALYSIS

Fault diagnosis of cross-condition gearbox based on IDANN

ZHAO Ling*, ZOU Jie, QIN Jiaji, WANG Hang

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Abstract

The transfer learning method has made great progress in solving the problem of unsupervised fault diagnosis of gearbox. However, due to the differences in the distribution of gearbox data, noise and interference, and the limitations of the model, most of the methods are not effective in migrating complex gearbox datasets, and there are still few studies on the interpretability of network inputs. In this paper, we propose an improved domain-adversarial neural network (IDANN). The improved time-frequency network is used as a feature extractor to provide interpretability and noise reduction when the signal is fed into the network, and then the class-level alignment method of the target domain is added to the domain adversarial network, and two classifiers are used to detect the target samples close to the decision boundary to enhance the migration performance. The effectiveness and reliability of IDANN are verified on the Southeast University gearbox dataset and straddle-type monorail gearbox, and the performance of IDANN under noise conditions is tested on the Case Western Reserve University bearing dataset, and the experimental results show that IDANN has excellent diagnostic performance and robustness.

Key words

transfer learning / interpretable networks / cross-condition fault diagnosis

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ZHAO Ling, ZOU Jie, QIN Jiaji, WANG Hang. Fault diagnosis of cross-condition gearbox based on IDANN[J]. Journal of Vibration and Shock, 2025, 44(9): 282-289

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