Noise Reduction and Reconstruction of Acoustic Emission Signals from Industrial Robot Gearboxes Based on Wavelet Transform and CEEMDAN

Abstract

In order to reconstruct the acoustic emission (AE) waveform from the original sound emission signals of industrial robot reducers, the method of generating simulated AE signal sources on industrial robot reducers using pencil-lead breaking is commonly employed. This paper proposes a reconstruction method combining wavelet decomposition and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Firstly, the motor drives the reducer at 900 r/min, and the original AE signals are acquired using a DS5-16C acoustic emission instrument at a sampling rate of 3 MHz. Secondly, the original AE signals are subjected to wavelet analysis to obtain sub-signals, and representative sub-signals are selected for CEEMDAN decomposition and reconstruction of the AE waveform. Finally, Fast Fourier Transform (FFT) and energy proportion analysis are performed on the reconstructed AE waveform to obtain its frequency domain characteristics and energy proportion features. Experimental results demonstrate that the frequency range of 150 kHz to 170 kHz dominates the reconstructed AE waveform, and the noise energy proportion in the signal decreases from 93% to 64%, validating the effectiveness of the proposed method in this paper.