Advanced Detection of REB Defects through Sound Emission using Envelope Analysis and Spectral Kurtosis

Abstract

Rolling elements bearings (REBs) are considered between the critical components in rotating machinery and their failures can provoke severe damage to the machine. Monitoring the condition of these components is essential to ensure the availability of the machine and improve its reliability. This article presents a low-cost acoustic approach based on the smartphone to monitor the bearing components. This approach stands on the use of a stethoscope connected to the smartphone via input Jack, to acquire the acoustic emission of the bearing at specific points. Firstly, the Hilbert transform (HT) was performed on acoustic signals to derive the envelope signal. Then, the Fast Fourier Transform (FFT) was applied to calculate the spectrum of the envelope signal. In the case of a noisy envelope spectrum where the fault signature is not noticeable, the Spectral kurtosis (SK) will be implemented to design an optimal filter to filter the acoustic signal using the Fast Kurtogram. After the filtering step, the process will be repeated to calculate the envelope spectrum. This study evaluates a defective bearing with a small inner race fault under different operating speeds (648, 1240, and 1816 rpm). Finally, the experimental results indicate that the proposed approach shows good results compared to the theoretical results for the early detection and identification of bearing failures. Furthermore, this technique is highly cost-effective and practical for rolling bearing condition monitoring.