Physics guided neural network: Remaining useful life prediction of rolling bearings using long short-term memory network through dynamic weighting of degradation process

Bearing is a common rotating component, the health status of bearing affects the operation and maintenance of equipment. Thus, the prediction of bearing remaining useful life is of great significance. The remaining useful life prediction based on neural network has black box property, which makes the prediction result may be contrary to the actual physical law. In this paper, a physics guided long short-term memory (LSTM) network is proposed based on the change trend of the time-frequency domain feature indicators of bearings in the process of degradation. Specifically, indexes such as monotonicity are used to select feature indicators that are highly trendy in the process of bearings degradation. On this basis, a regularization term based on the consistent variation of the feature indicators and the remaining useful life (RUL) in the process of bearing degradation is constructed to make the result of the network more consistent with the actual physical law. Meanwhile, the variation of feature indicators is used as dynamic weight to enhance the potential physical consistency. The experimental comparison results show that the prediction results of the network are more accurate and consistent with the actual physical laws with the guidance of physical prior knowledge.