Cross-Conditions Fault Diagnosis of Rolling Bearing Based on Transitional Domain Adversarial Network

To address the poor performance of traditional rolling bearing fault diagnosis models in cross-condition tasks due to significant feature differences, a transitional domain adversarial network (TDAN) is proposed in this article. This model initially builds a multichannel, multifeature extractor to obtain the frequency domain phase spectrum of vibration signals. It then integrates this data with spectral and time-domain features to extract deep, domain-invariant characteristics from various perspectives. Transition units are also designed to derive both domain and class transitional zones. The domain transitional zone aims to mitigate the loss of certain features caused by forced alignment between source and target domains. Meanwhile, the class transitional zone enhances feature granularity from the perspective of interclass variation, thereby improving class-specific representation, smoothing the adversarial process, and boosting model generalization. Additionally, to address the target-oriented adversarial loss function, a readversarial module is introduced. This process equips the model with the capability to escape local optima and optimize parameters adaptively during training, resulting in stronger robustness and adaptability. Comparative experiments with other unsupervised domain adaptation (UDA) methods on two bearing datasets demonstrate TDAN's effectiveness and superiority in rolling bearing cross-condition fault diagnosis. It also demonstrates the model's potential for application in real industrial scenarios where varying operating conditions lead to differences in vibration signals.