A novel domain adaptive method for gearbox fault diagnosis using maximum multiple-classifier discrepancy network

Domain adaptive gearbox fault diagnosis methods have made impressive achievements for the past several years. However, most of the traditional domain adaptive methods have significant limitations under fluctuating operating conditions. The acquired acceleration signals will result in different signal vibration spectra and peak vibration amplitudes due to the different working conditions between the source and target domains. There is an obvious discrepancy between the distribution of fault samples in the source domain and the target domain, which in turn makes it difficult to classify the target domain samples with fuzzy fault category boundaries. Therefore, how to measure the discrepancy between two distributions has been an important research direction in machine learning. A good metric helps to discover better features and build better models. In this paper, a novel domain adaptive method for gearbox fault diagnosis using maximum multiple-classifier discrepancy network (MMCDN) is proposed. The sparse stack autoencoder is used by the MMCDN as a feature extractor for fault feature extraction, and a kind of composite distance is adopted for domain discrepancy measurement of source and target domain features for domain alignment. Then the extracted features are input into a three-classifier of the model for adversarial training. The trained model classifiers have high performance in fault classification. The combination of domain adaptation and multi-classifier discrepancy output can effectively solve the impact of working condition changes and the misclassification problem for fuzzy samples with class boundaries. Experimental validation with two planetary gearbox datasets shows that the MMCDN has more favorable diagnostic accuracy and performance than other methods.