A Novel Indicator to Improve Fast Kurtogram for the Health Monitoring of Rolling Bearing

Abstract

Envelope demodulation based on vibration data is widely used for the fault detection of rolling element bearing, which yet largely relies on the high signal to noise ratio of signal. In practical scenarios, because of the existence of various interfering components, it is necessary to estimate the fault-sensitive frequency band for feature enhancement. However, many of current approaches are likely to produce misleading results for that they are robust only for part of these interferences. Facing with this problem, an alternative method, named as ALKurtogram, is proposed by aligning the frequency division strategy of traditional fast kurtogram (FK) with a new indicator, the averaged local kurtosis (ALK). ALK measures both the local and overall impulsiveness of objective component, which intrinsically makes up for some drawbacks of previous methods under multi-interference condition for fault feature extraction. The inherent nature of ALK also allows it to be compatible with the time-varying speed, hence more widespread industrial applications can be achieved. Furthermore, a harmonic energy index (HEI) is defined by following the result of ALKurtogram to ascertain the order of principle component for bearing status recognition. The effectiveness and superiority of proposed method are validated experimentally by comparing with FK, the results prove that it is a powerful tool for reliable monitoring of bearing.