Remaining Useful Life prediction of rolling bearings based on risk assessment and degradation state coefficient

Prediction of Remaining Useful Life (RUL) of bearings is very important for the condition-based maintenance of the rotating machinery. In order to predict the RUL more accurately, an approach of RUL prediction based on risk assessment and degradation state coefficient is proposed. The Mahalanobis Distance (MD) is calculated depending on the characteristics of vibration signals in the time domain and time-frequency domain. The features in the time-frequency domain are extracted by using Variational Mode Decomposition-Singular Value Decomposition (VMD-SVD). The monotonously increasing Health Indicator (HI) is obtained by using MD1-CUMSUM. The risk assessment is proposed to adaptively determine the thresholds of initial fault and failure, which is a trade-off between the false alarm rate and sensitivity. The RUL prediction of the testing bearing is completed based on the Genetic Algorithm-Support Vector Regression (GA-SVR) and Modified Health Indicator (MHI) with the degradation state coefficient. The proposed approach is verified by using two experimental vibration signal datasets. The results show that the proposed method has good capability to predict the RUL of rolling bearings.