The spectral kurtosis: a useful tool for characterising non-stationary signals

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Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K-Nearest Neighbor Distance Analysis (2016, methodology): “SK has been used as an effective method for detecting localized faults from nonstationary signals [23].”
Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators (2018, methodology): “In addition, according to the blind fault component separation method proposed by Antoni [15], more advanced signal processing methods should be used in themachine degradation process to separate 672 VOLUME 6, 2018 bearing fault signals with gear signals and detect early bearing and gear faults.”
A New Improved Kurtogram and Its Application to Bearing Fault Diagnosis (2015, background): “Randall and Antoni [5] gave a detailed tutorial on rolling element bearing diagnosis and consider envelope analysis (high frequency resonance technique) as fundamental and effective method for bearing fault detection.”
An extensive review of vibration modelling of rolling element bearings with localised and extended defects (2015, methodology): “monitored to detect the presence of a defect using appropriate condition-based (vibration and acoustic) 14 diagnostic techniques [3, 6–18].”