Periodicity measure of cyclo-stationary impulses based on low sparsity of Gini index and its application to bearing diagnosis.

A key problem in the fault diagnosis of rolling element bearings is the extraction of features of repetitive transients from vibration signals. The accurate evaluation of maximizing spectral sparsity under complex interference conditions for measuring the periodicity of transients is typically difficult to implement. Accordingly, a novel periodicity measurement approach was designed for time waveforms. According to the Robin Hood criteria, the Gini index of a sinusoidal signal has a stable low sparsity. The periodic modulation of cyclo-stationary impulses can be represented by several sinusoidal harmonics based on envelope autocorrelation and bandpass filtering. Thus, this low sparsity of Gini index can be used to evaluate the periodic strength of modulation components. Finally, a sequential feature evaluation method is developed to extract periodic impulses accurately. The proposed method is tested on simulation and bearing fault datasets and compared with the state-of-art methods so to assess its effectiveness.