A Remaining Useful Life Prediction Approach Based on Low-Frequency Current Data for Bearings in Spacecraft

Remaining useful life (RUL) prediction of rolling bearings brings benefits for maintenance of spacecrafts. Vibration signals are widely used for RUL prediction. However, under some situations such as high-speed rotation of bearings, vibration signals are quite easily disturbed by noise and might be tough to collect due to inappropriate installation of accelerometers. Therefore, in this paper, stator current signals are considered as health indicator for bearing RUL prediction. Based on stator current signals, feature extraction and trajectory tracking of signals suffer two challenges: 1) degradation tracking of downlinked stator current data is restricted due to low-frequency downlink from spacecrafts to stations; 2) the accuracy of health state estimation is constrained by artificial feature selection and prior knowledge about bearings. To overcome these issues, a novel RUL prediction approach is proposed in this work. An adaptive feature selection strategy based on autoregression model and backpropagation neural network is applied. Finally, an improved RUL prediction framework is introduced under down-sampled signals, which combines compressed sensing and deep learning model. A case from Paderborn university and a case from life test of bearing on control moment gyro were investigated. Experimental results support the validity of proposed approach in terms of lower prediction error than related works.