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A Zero-Sample Fault Diagnosis Method Based on Transfer Learning
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AI
DOI：10.1109/TII.2024.3405634.png)
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摘要
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Zero-sample fault diagnosis (ZSFD) achieves remarkable success and has attracted considerable attention. However, existing methods suffer from the limitations in fault attribute labeling and feature extraction, leading to poor generalization and robustness. To be specific, fault attribute labeling that depends on expert knowledge is time-consuming and laborious. Fault feature extraction is carried out in one projecting space, and the useful knowledge of target data is ignored, which results in nonoptimal ZSFD performance. To tackle the above problems, a novel ZSFD based on transfer learning is proposed. First, a shared knowledge dictionary that automatically learns from source data with labels is transferred into the target data, which reduces the dependence of ZSFD on fault description. Second, a novel multiclass space projection model is designed to obtain the discriminative fault features. Finally, the pseudolabel mechanism is introduced to excavate the interclass and intraclass information in the target domain. The experiment results on the Tennessee-Eastman process and a real hot roll of steel process show the effectiveness of our method as well as its superiority.
Keyword:
Fault diagnosis
Zero-shot learning
Transfer learning
Dictionaries
Feature extraction
Animals
Accuracy
feature learning
transfer learning
zero-sample learning
期刊
IF:
9.9
论文数:
8.6K
被引数:
6.0W
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