Rolling Bearing Fault Diagnosis Method Based On Dual Invariant Feature Domain Generalization

In industrial production, complex working conditions often result in poor generalization of fault diagnosis models. Consequently, we propose a method called Dual Invariant Feature Domain Generalization (DIFDG) for fault diagnosis of rolling bearings. This method is built upon the knowledge distillation framework, wherein phase information is extracted from one-dimensional signals using the Fourier transform. The phase information remains insensitive to domain changes and serves as the training data for the teacher network, enabling it to learn internally-invariant features. Simultaneously, employing the knowledge distillation technique, the student network learns internally-invariant features from one-dimensional time series signals. To prevent information loss, two-dimensional time-frequency diagram data is incorporated into the student network training. The student network is trained to acquire mutually-invariant features using a class-related loss function for data from various domains. This method trains a student network to extract dual invariant features from the data, enhancing the generalization to applications involving unknown data. Experimental results demonstrate that this approach outperforms other representative methods in both the CWRU and NEFU datasets.