非均匀监测条件下滚动轴承剩余寿命预测方法

The modeling of degradation processes relies on the discretization of time and amplitude, and most of the existing degradation modeling methods are based on the assumption of uniform dispersion of time. However, in the daily operation and maintenance of the equipment, due to factors such as sensor failures or operator errors, the available monitoring data can be nonuniform (such as condition monitoring data of rolling bearings as key components of rotating machinery), which results in additional deviations in the degradation model when updating parameters and predicting the remaining life (RUL). Aiming at this problem, a RUL prediction approach with nonuniform monitoring conditions for rolling bearings is proposed. Firstly, the Brownian motion term associated with monitoring intervals is constructed to accurately characterize the temporal variability of random parameters of the degradation model with nonuniform intervals. Then, based on the expectation maximization (EM) algorithm and the square-root cubature Kalman filter (SCKF), a dynamic parameter inference method of the degradation model with nonuniform intervals is proposed, and the degradation state and RUL adaptive estimation of rolling bearings are accomplished. The effectiveness of the proposed approach for predicting the RUL is verified by means of rolling bearing full life test examples. The results show that under nonuniform monitoring conditions, the proposed approach obtains higher prediction accuracy and better fitting performance compared with other exponential degradation models.