An Optimized Kurtogram Method for Early Fault Detection of Rolling Element Bearings Using Acoustic Emission

Abstract

Bearings are the basic components of rotating machinery and their integrity is the key to ensuring the operational stability and work reliability of machines. Compared with traditional vibration analysis, acoustic emission (AE) has some unique advantages, such as higher sensitivity to low-speed rotating mechanical defect detection and more potential for early fault detection. However, the AE signals collected from bearing with incipient fault always include heavy noise levels, reducing the capability of early defect detection. Therefore, this paper proposes an optimized Kurtogram method for incipient defect detection of bearings, which combines autocorrelation function, Shannon entropy and Kurtogram to identify early localized defects in AE signals. The major innovations are as follows: (i) The autocorrelation function (ACF) is adopted to process the envelope of all wavelet packet node signals to highlight the periodic pattern in the AE signal, (ii) kurtosis-to-Shannon entropy ratio (KSR) is introduced to improve the capability to detect bearing fault characteristics in low signal-to-noise ratio (SNR) signals. Simulated AE signals and real bearing fault signals were used to evaluate the effectiveness of the proposed method. The results show that the proposed method can detect early defects of bearings and is superior to other Kurtogram-based approaches.