Abstract
During the operation of industrial equipment, substantial volumes of both normal and faulty unlabeled data are frequently generated. Traditional data-driven models are often susceptible to significant performance degradation in the presence of large amounts of unlabeled data, and their diagnostic decision-making processes typically lack robust physical interpretability. To address these challenges, this paper proposes an unsupervised Physical Information Neural Network (PINN) that utilizes the dynamic updating of pseudo-labels for the intelligent diagnosis of industrial bearings. The PINN is designed to compute the gradient of each network layer parameter through backpropagation, effectively combining data-driven learning with physical constraints and incorporating a regularization mechanism to enhance generalization capabilities. In the experiments, wavelet time-frequency diagrams are constructed based on three distinct fault states alongside the normal state of rolling bearings, elucidating the time-frequency characteristics under various operational conditions. Additionally, initial pseudo-labels are generated using the fault octave amplitude ratio. A dynamic pseudo-label update mechanism is implemented during the training phase, incorporating adaptive correction based on physical constraints. This mechanism ensures that the pseudo-labels are refined adaptively to accurately represent the current physical signal state within an unsupervised context, thereby improving the model’s self-learning capability and fault diagnosis accuracy. Ultimately, the proposed model is validated on a publicly available bearing dataset, demonstrating robust fault recognition performance and strong physical interpretability, even in scenarios characterized by limited labeled data and substantial background noise. The model exhibits promising potential for practical applications in the field of industrial diagnostics.
Get full access to this article
View all access options for this article.