Domain Adaptation With Joint Distribution Alignment Adversarial Learning for Open-Set Bearing Intelligent Fault Diagnosis

As a key component indispensable to the normal operation of rotating machinery, the fault diagnosis of rolling bearings is beneficial to ensure the normal operation of industrial production. Many existing fault diagnosis methods based on domain adaptation rely on the assumption that the training and testing data share the same label type, whereas in practical industrial scenarios, due to the impossibility of predicting failure modes in the testing phase, it is highly likely that unknown fault types will appear during the test phase that did not occur during the training phase. To deal with this Open-Set Fault Diagnosis (OSFD) problem, this study proposes a domain adaptation method based on joint distribution alignment adversarial learning. This method is based on a novel triple-indicator metric that utilizes three estimation parameters, mean, variance, and covariance, to better measure distribution di screpancy, and combines the joint distribution alignment domain adaptation technique to align the distribution of training and testing data. Furthermore, we propose an Enhanced Feature Extraction and Classification Adversarial Neural Network (EFECANN) that can efficiently process vibration signals, which consists of three parts: feature extractor, domain discriminator and label classifier. These three parts are combined to promote the effective extraction of fault features for classification and diagnosis. Bearing datasets from Paderborn University (PU) and Case Western Reverse University (CWRU) are used to carry out comparative and ablation experiments to validate the proposed method for the OSFD problem, and the experimental results show that the proposed method outperforms other typical methods in fault diagnosis.