Experimental Investigation of the Diagnosis of Angular Contact Ball Bearings Using Acoustic Emission Method and Empirical Mode Decomposition

Abstract

Early detection of angular contact bearings, one of the important subsets of rolling element bearings (REBs), is critical for applications of high accuracy and high speed performance. In this study, acoustic emission (AE) method was applied to an experimental case with defects on angular contact bearing. AE signals were collected by AE sensors in different operating conditions. Signal to noise ratio (SNR) was calculated by kurtosis to entropy ratio (KER), then acquired signals were denoised by empirical mode decomposition (EMD) method, and optimal intrinsic mode function (IMF) was selected by the proposed method. Finally, envelope spectrum was applied to the denoised signals, and frequencies of defects were obtained in different rotating speeds, loadings, and defect sizes. For the first time, a small defect with width of 0.3 mm and loading of 475 N was detected in early stage of 0.04 KHz. Moreover, a comparison between theoretical and extracted defect frequencies suggested that our method successfully detected localized defects in both inner and outer race. Our results show promise in detecting small size defects in REBs.

Experimental investigation on time-domain features in the diagnosis of rolling element bearings by acoustic emission

“…We recently studied the fault detection of angular contact ball bearings by AE and EMD methods. The results revealed the effectiveness of the AE method in detecting small defects on both the inner and outer races of the bearings (Tabatabaei et al, 2020), and it was shown that the AE method can detect both small and large defects even in the cases of small defects and low SNR AE signals. It is worth to mention, the AE method has some advantages over other methods, such as vibration analysis, which enables it to detect defects in their early stages (more sensitive to the onset and growth of defects), and it is also applicable in bearings with low working speed (Al-Ghamd and Mba, 2006; Sako and Yoshie, 2010).…”

A hierarchical deep learning framework for combined rolling bearing fault localization and identification with data fusion

“…A wide range of studies have dedicated on advancing the bearing fault diagnosis capacity, where the machine learning approaches play an important role (Dong et al, 2021; Duan et al, 2021; Zhou et al, 2017). The underlying idea of these approaches is to elucidate the intrinsic correlation between the faults and different types of measurements, such as vibration, acoustic emission and eddy current and so on (Aasi et al, 2021; Ben Ali et al, 2015; Chen et al, 2016; De Moura et al, 2011; Jiang et al, 2019; Pandya et al, 2013; Tabatabaei et al, 2020; ). Among them, vibration signals are most widely used for bearing fault diagnosis because of the low instrumentation cost and sufficient fault-related signatures contained (Ben Ali et al, 2015; Chen et al, 2016; De Moura et al, 2011; Liang and Zhou, 2021).…”

Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection

“…Tabatabaei et al performed signal analysis with acoustic emission method (AEM) to detect defects on angular contact bearings. The authors applied feature extraction with empirical mode decomposition (EMD) algorithm on the signals obtained with AEM [ 21 ]. EMD is used to analyze nonlinear and nonstationary signals by separating them into components with different resolutions [ 22 ].…”

“…A study on the determination of blood pressure measurement time has not been found so far. For [21]. EMD is used to analyze nonlinear and nonstationary signals by separating them into components with different resolutions [22].…”