A new health indicator for rolling bearings based on impulsiveness and periodicity of signals

Abstract The early fault diagnosis of rolling bearings is of great significance. Most existing methods are insensitive to the early faults of bearings and unstable for different bearings. In order to solve these issues, a new health indicator based on the impulsiveness and periodicity of signals is proposed to diagnose bearing faults and identify initial degradation points (IDPs). First of all, the time domain signal is divided into multiple signal blocks. Secondly, the median local kurtosis (MLK) and fault characteristic order point amplitude (FAMP) of each signal block are calculated respectively to represent the impulsiveness and periodicity of the signal. By combining MLK with FAMP, MLK-FAMP is obtained to screen out the signal blocks containing fault information. Lastly, the FAMP of screened signal blocks is calculated by order analysis, which contains four components corresponding to four faults. The early failure type of bearings is identified according to the trend of these four components of FAMP. A relative similarity principle is applied to corresponding fault components to obtain the final health indicator, namely the MLK-FAMP-health indicator. The proposed method is validated in two cases and compared with indicators constructed using other methods. The results show that this method is able to precisely diagnose early faults and accurately identify the IDPs of bearings.