DTPNet: A Physics-Guided Dynamic Tensor Projection Network for Bearing Fault Diagnosis Under Variable Speed Conditions

Intelligent rolling bearing fault diagnosis methods under variable-speed conditions have made significant progress, yet they still suffer from limited physical interpretability of extracted features and high sensitivity to speed fluctuations. To overcome these issues, a physics-guided dynamic tensor projection network (DTPNet) for variable-speed fault diagnosis is proposed. First, the adaptive wavelet feature extractor is specially constructed using a learnable wavelet transform and an attention mechanism to extract physically meaningful fault features across varying rotational speeds. Second, the condition-invariant dynamic subspace projection (CDSP) module is designed to dynamically generate a learnable projection matrix, mapping features into a condition-invariant and fault-relevant subspace. Third, a novel dynamic decoupling projection loss is proposed as a global constraint to guide the training of DTPNet. The synergistic interaction of these modules enables DTPNet to achieve reliable fault diagnosis under complex variable operating conditions. Experimental results on two case studies indicate that DTPNet significantly outperforms other state-of-the-art methods in terms of diagnosis accuracy, robustness, and interpretability.