Noise Reduction of Welding Crack AE Signal Based on EMD and Wavelet Packet The acoustic emission (AE) signal collected by a sensor in the welding process has an overlapping frequency band and weak characteristics under a complex noise background. It is difficult for the wavelet noise reduction method, with single basis function, to effectively match the different characteristic information of the welding crack AE signal. Taking into account the adaptive decomposition characteristics of Empirical Mode Decomposition (EMD), a novel wavelet packet noise reduction method for welding AE signal was proposed. The welding AE signal was adaptively decomposed into several Intrinsic Mode Functions (IMFs) by the EMD. The effective IMFs were selected by the frequency distribution characteristics of the welding crack AE signal. A wavelet packet, with a specific basis function, was subsequently performed on the effective IMFs, which were reconstructed to be the welding crack AE signal. The simulated and experimental results indicated that the proposed method can effectively achieve noise reduction of the welding crack AE signal, which provided a mean for structure crack detection in the welding process.

A study on rotate vector reducer performance degradation based on acoustic emission techniques This paper presents a performance degradation analysis for rotate vector (RV) reducer using acoustic emission (AE) techniques. Unlike the AE signals from fixed gear reducer that are steady and have unchanged route, the AE signals from RV reducer have variant propagation paths and strengths. This work investigates the kinematics model using AE techniques to solve the inherent issues of RV reducer performance degradation analysis, such as signal variabilities and inappropriate thresholds. Firstly, the propagation path of AE signals within the crankshaft is developed. Secondly, the attenuation trend of AE signals is exponentially formulated based on the corresponding propagation curve. The proposed method has been evaluated on the developed RV reducer platform. AE signals were collected under various working conditions, i.e., four abrasion states with eight types of loads and five rotation speeds. The experimental analysis on typical parameters (e.g., signal energies) proves the effectiveness and accuracy of the developed method based on AE techniques.