Abstract
Due to the mechanical equipment working under variable speed and load for a long time, the distribution of samples is different (domain shift). The general intelligent fault diagnosis method has a good diagnostic effect only on samples with the same sample distribution, but cannot correctly predict the faults of samples with domain shift in a real situation. To settle this problem, a new intelligent fault diagnosis method, domain adaptation network with double adversarial mechanism (DAN-DAM), is proposed. The DAN-DAM model is mainly composed of a feature extractor, two label classifiers and a domain discriminator. The feature extractor and the two label classifiers form the first adversarial mechanism to achieve class-level alignment. Moreover, the discrepancy between the two classifiers is measured by Wasserstein distance. Meanwhile, the feature extractor and the domain discriminator form the second adversarial mechanism to realize domain-level alignment. In addition, maximum mean discrepancy (MMD) is used to reduce the distance between the extracted features of two domains. The DAN-DAM model is verified by multiple transfer experiments on some datasets. According to the transfer experiment results, the DAN-DAM model has a good diagnosis effect for the domain shift samples. Moreover, the diagnostic accuracy is generally higher than other mainstream diagnostic methods.
Highlights
In the process of operation, rotating machinery equipment is often subjected to sudden load increase and load reduction, resulting in stress and speed change of rotating machinery equipment [1]
It can be seen from the experimental results that the domain adaptation network with double adversarial mechanism (DAN-DAM) model has a better diagnostic effect for the domain shift samples and the diagnostic accuracy is generally higher than other mainstream diagnostic methods, which more strongly proves the superiority of the DAN-DAM model
This paper proposes a DAN-DAM model for the inconsistency of sample distribution under variable working conditions
Summary
In the process of operation, rotating machinery equipment is often subjected to sudden load increase and load reduction, resulting in stress and speed change of rotating machinery equipment [1]. As the most commonly used transfer learning method at present, domain adaptation does not require that training data and test data have the same distribution This method successfully solves the dilemma faced by the current mechanical fault diagnosis field [20]. This paper proposes a new fault method, namely, domain adaptation network with double adversarial mechanism (DAN-DAM) This method takes both domain-level alignment and class-level alignment into account and achieves satisfactory diagnostic results. The proposed method was verified by multi-group transfer experiments and compared with other mainstream intelligent fault diagnosis methods It can be seen from the experimental results that the DAN-DAM model has a better diagnostic effect for the domain shift samples and the diagnostic accuracy is generally higher than other mainstream diagnostic methods, which more strongly proves the superiority of the DAN-DAM model.
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