Acoustic Emission Signal Feature Extraction for Bearing Faults Using ACF and GMOMEDA

Abstract

In industry, rolling bearing damage acoustic emission (AE) signals are interfered with by complex transmission paths and strong noise. The signal-to-noise ratio of the AE signal is low. The bearing periodic fault pulse is weak, and fault feature extraction is challenging. To address these issues, combined with the characteristics of impulsiveness and rapid attention of the AE signal, an enhancement of the bearing weak fault signal based on the autocorrelation function (ACF) and improved multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) method is proposed in this contribution. Firstly, in low signal-to-noise ratio, the target vector of the MOMEDA method is not optimal, and the diagnostic accuracy is low. To address this problem, this paper improves MOMEDA by using the gradient descent method, called GMOMEDA. Rolling bearing fault AE pulse signals are enhanced. Then, a method combination of ACF and GMOMEDA highlights the periodic elastic wave in the signal. Finally, the enhanced AE signal is processed by envelope demodulation to extract the frequency of the bearing fault signal. The experimental results show that the performance of the ACF-GMOMEDA method is better than the other five methods. The frequency features of bearing fault AE signal can be accurately extracted.