Use of Spectral Kurtosis for Improving Signal to Noise Ratio of Acoustic Emission Signal from Defective Bearings

Abstract

The use of Acoustic Emission (AE) to monitor the condition of roller bearings in rotating machinery is growing in popularity. This investigation is centred on the application of Spectral Kurtosis (SK) as a denoising tool able to enhance the bearing fault features from an AE signal. This methodology was applied to AE signals acquired from an experimental investigation where different size defects were seeded on a roller bearing. The results suggest that the signal to noise ratio can be significantly improved using SK.

Helicopter main gearbox bearing defect identification with acoustic emission techniques

“…Thus, the same bearing force will excite same frequency ranges. The center frequency and bandwidth are useful to design band pass filter to improve AE signal to noise ratio [6], [7] Envelope analysis has been extensively utilized in vibration analysis for diagnosing bearings and gearboxes defects [8], [9]. Envelope analysis is based on the principle of identifying frequencies of the impacts, which results from defects excited resonance.…”

Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications

“…We can conclude that SK is mostly used to generate filters to extract the most impulsive part of the signal from background noise or other interactions. In general, to use kurtosis as a diagnostic tool can be also summarized according to the following categories: kurtosis of a filtered temporal signal [33,34,36,37,59,69]; spectral kurtosis as a tool for selection of frequency band for demodulation [42][43][44][46][47][48]53,57,58,64,65,[71][72][73]75,76,87,[91][92][93]97,98]; filtration of residual signals [68,74,91,94] and transient event detection [41,60,[99][100][101][102] as well as harmonic component detection [103]. Moreover, pre-whiten the signal using some techniques, for instance, an AR model technique can further enhance the impulsiveness.…”

Planetary bearing defect detection in a commercial helicopter main gearbox with vibration and acoustic emission

“…46 More recently, the use of SK and adaptive filters has been employed to facilitate the diagnosis of machine faults with AE. [47][48][49] Signal processing and data analysis Bearing and gear fault identification involves the use of various signal processing algorithms to extract useful diagnostic information from measured vibration or AE signals. Traditionally, analysis has been grouped into three classes: time domain, frequency domain and time-frequency domain.…”

“…AE was originally developed for non-destructive testing of static structures; however, in recent times, its application has been extended to health monitoring of rotating machines and bearings. 46–49 In machinery monitoring applications, AEs are defined as transient elastic waves produced by the interface of two components or more in relative motion. 50,51 AE sources include impacting, cyclic fatigue, friction, turbulence, material loss, cavitation, leakage and so on.…”