Novel adaptation of the spectral kurtosis for vibration diagnosis of gearboxes in non-stationary conditions

In this paper, the adaptation of spectral kurtosis technology is proposed, demonstrated and experimentally validated. Raw data signals were collected from a single-stage gearbox run in different combinations of speed and load, after which time synchronous averaging was used to leave the classical residual signal once meshing harmonics were removed. Each data file is split into many individual realisations based on the time taken for the time synchronous average to converge on stable values, after which the short-Time Fourier transform is used to calculate the spectral kurtosis for each realisation. The effects of adapting spectral kurtosis technology parameters such as the resolution and threshold used in creating a Wiener filter are evaluated, showing the effects on the consistent frequency bands identified throughout the realisations. Taking a baseline set of processing parameters, the probability of correct diagnosis was calculated using a three-stage decision-making technique incorporating the k-nearest neighbour and cluster analysis methods. Adaptation of the spectral kurtosis technology is then shown to dramatically improve the probability of correct diagnosis, highlighting that each speed and load case requires different resolution and threshold values to return the optimal results.