Physics-Informed Deep Neural Network for Bearing Prognosis with Multisensory Signals
Keywords:deep learning;physics-informed neural network (PiNN);Prognostics and Health Management (PHM); remaining useful life
Prognosis of bearing is critical to improve the safety, reliability and availability of machinery systems, which provides the health condition assessment and determines how long the machine would work before failure occurs by predicting the remaining useful life (RUL). In order to overcome the drawback of pure data-driven methods and predict RUL accurately, a novel physics-informed deep neural network, named degradation consistency recurrent neural network, is proposed for RUL prediction by integrating the natural degradation knowledge of mechanical components. The degradation is monotonic over the whole-life of bearings, which is characterized by temperature signals. To incorporate this knowledge of monotonic degradation, a positive increment recurrence relationship is introduced to keep the monotonicity. Thus, the proposed model is relatively well-understood and capable to keep the learning process consistent with physical degradation. The effectiveness and merit of the RUL prediction using the proposed method are demonstrated through vibration signals collected from a set of run-to-failure tests.