A Hybrid Physical Damage Neural Network for Wear Prediction of Self-Lubricating Bearings

Abstract Self-lubricating bearings are widely used in aerospace, marine, and other fields due to their excellent performance. Accurate wear prediction for self-lubricating bearings is crucial for ensuring reliability and safety. However, achieving both physical interpretability and high accuracy in predictive models remains a challenge, as these bearings typically operate under varying load conditions and in high-noise environments. In this article, a hybrid physical damage neural network is proposed for wear prediction. First, a “physics neuron operator” based on the Archard wear model is designed and embedded into the network to directly compute wear depth. Second, a cumulative damage law is introduced into…

• NN
  National Natural Science Foundation of China

  Awards: No. 92467101 and 52275499, 52275499, 92467101