Deep causal factorization network: A novel domain generalization method for cross-machine bearing fault diagnosis

Jia, Sixiang; Li, Yongbo; Wang, Xin‐Yue; Sun, Dingyi; Deng, Zichen

doi:10.1016/j.ymssp.2023.110228

Cited by 16 publications

(1 citation statement)

References 31 publications

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“…(3) Artificial intelligence Fault diagnosis methods based on artificial intelligence can be divided into fault diagnosis methods based on expert systems [3], diagnosis methods based on shallow machine learning [4,5], and fault diagnosis methods based on deep learning [6,7]. The diagnosis method based on the expert system uses expert knowledge and experience to form a knowledge base, so the diagnostic model has the judgment ability similar to that of experts and can take into account the uncertain factors in the future and the special situation of the diagnostic object, but it requires a large amount of knowledge accumulation and revision, and it is difficult to establish a perfect diagnostic knowledge base.…”

Section: Current Research Status Of Fault Diagnosis Methodsmentioning

confidence: 99%

Fault Diagnosis Method of Box-Type Substation Based on Improved Conditional Tabular Generative Adversarial Network and AlexNet

Liu,

Zhou,

Zhang

et al. 2024

Applied Sciences

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To solve the problem of low diagnostic accuracy caused by the scarcity of fault samples and class imbalance in the fault diagnosis task of box-type substations, a fault diagnosis method based on self-attention improvement of conditional tabular generative adversarial network (CTGAN) and AlexNet was proposed. The self-attention mechanism is introduced into the generator of CTGAN to maintain the correlation between the indicators of the input data, and a large amounts of high-quality data are generated according to the small number of fault samples. The generated data are input into the AlexNet model for fault diagnosis. The experimental results demonstrate that compared with the SMOTE and CTGAN methods, the dataset generated by the self-attention-conditional tabular generative adversarial network (SA-CTGAN) model has better data relevance. The accuracy of fault diagnosis by the proposed method reaches 94.81%, which is improved by about 11% compared with the model trained on the original data.

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