基于 IKF-ARIMA-NARNN 的滚动轴承噪声环境下寿命预测方法

In view of the fact that the performance characteristics of rolling bearings vibration signals are inevitably affected by environmental noise and lack of Markov characteristics, a life prediction method of rolling bearings in noisy environment based on IKF-ARIMA-NARNN was proposed. Firstly, considering that the Kalman filter (KF) denoising ignored the correlation of data, a denoising method based on incremental Kalman filter (IKF) was proposed. Secondly, due to the evolution process of bearing performance characteristic parameters that fails to meet the Markov characteristics sometimes, an analysis model of the evolution process of bearing performance characteristic parameters based on the autoregressive integrated moving average (ARIMA) with nonlinear error term was established. At the same time, the dynamic nonlinear autoregressive neural network (NARNN) was used to estimate the nonlinear random error term of the degradation model, so as to realize the life prediction of rolling bearings. Finally, the effectiveness and applicability of the proposed method was verified by the case analysis of rolling bearing engineering, and the prediction accuracy was at least 26.75% and 51.25% higher than the traditional method.