Health Indicator Construction Based on MD-CUMSUM with Multi-Domain Features Selection for Rolling Element Bearing Fault Diagnosis

The initial fault signal of rolling element bearing is extremely weak and could be easily masked by strong background noise. Different features of vibration signal can be different sensitivity to initial fault and performance degradation. Moreover, individual features cannot reflect bearing fault rationally and these features reveal non-monotonic behavior when the bearing condition deteriorates. A Health Indicator (HI) is proposed based on Mahalanobis Distance and Cumulative Sum (MD-CUMSUM). The time-frequency domain features extracted through Singular Value Decomposition based on Variational Mode Decomposition (VMD-SVD) and several optimal time domain features are used to calculate Mahalanobis Distances (MDs). The coarse-to-fine diagnosing strategy is proposed to determine the initial fault of rolling bearing. The obtained HI is utilized to estimate the different performance degradation stages of the bearing depending on the thresholds. This method is verified by utilizing two different experiments. The results demonstrate that the approach has the capability of estimating initial fault and determining degradation stages of bearing.