Rolling Element Bearing Degradation Prediction Using Dynamic Model and an Improved Adversarial Domain Adaptation Approach

Rolling element bearing degradation prediction is an important issue in rotating machinery. With the rapid development of artificial intelligence (AI), AI-based bearing degradation prediction has aroused extensive attention. However, current methods rely on whole life cycle data, which is quite difficult to acquire in real industrial scenarios. To solve this problem, a rotor-bearing dynamic model is built to generate simulation signals for a range of spall sizes, and an improved domain adversarial neural network is proposed to transfer degradation knowledge from simulation data to experimental data. To be specific, complete simulation data is used to pre-train a network for learning comprehensive degradation knowledge, and guides the extracted high-level features in the adversarial domain adaptation stage to align with it as an additional optimization item. The proposed approach is verified on bearing degradation datasets under different working conditions, and results show that the proposed approach can successfully predict bearing degradation progress only with some early stage experimental data.