# Noise Reduction Based on a CEEMD-WPT Crack Acoustic Emission Dataset
## 1 Introduction  
As a kind of damage, cracks seriously affect the performance of a structure, exist in all walks of life, and can be a hidden danger for a subsequent accident. Acoustic emission (AE) refers to the elastic wave released through the structural medium when microcracks occur [1, 2]. AE is a structural health monitoring (SHM) technology [3], which has the advantages of online monitoring, high sensitivity, and early and rapid defect detection [4, 5, 6, 7]. It has been widely used in machinery, geology, materials, and other fields, and has obtained a lot of achievements [8]. The shear fracture of material structures usually begins with microcracks produced by stretching [9], so it is of great significance to detect and classify early cracks. [PERSON] et al. [10] proposed a method combining acoustic emission sensors and vibration monitoring to study the corrosion cracking of reinforced concrete. They proved that acoustic emission technology can accurately predict concrete cracking, whereas vibration monitoring is not sensitive to early defects but can locate damage in a wider range, and the two methods can complement each other. [PERSON] et al. [11] distinguished the AE signals of cracking and the fiber desticking of steel-fiber concrete through principal component analysis, and found that the \"rise time and count\" and \"duration and amplitude\" of the two signals hadthe most obvious difference. [PERSON], [PERSON] et al. [12] studied acoustic emission signals in the Laser Powder Bed Fusion (L-PBF) process and used the machine learning model to classify crack events and noise, but the effect of this method in the low signal-to-noise ratio environment has not been verified. [PERSON] et al. [13] used the traditional machine learning model and convolutional neural network (CNN) model to study the classification effect of defects, such as Laser-Assisted Direct Energy Deposition (LDED) cracks, under different characteristic parameters, and the results showed that the Mel Frequency Cepstral Coefficients (MFCC)-CNN proposed in the paper had the best effect. The application of acoustic emission technology is also difficult, and the recording of acoustic emission signals may be affected by the environment and contact area to produce noise [14]. For this reason, scholars have proposed a variety of noise reduction methods. The most commonly used noise reduction algorithms are wavelet and Empirical Mode Decomposition (EMD) and their derivatives, such as WPT, Ensemble Empirical Mode Decomposition (EEMD), CEEMD, CEEMDAN, and Variational Mode Decomposition (VMD). Among them, [PERSON] et al. [15] proposed a new threshold function and applied it in wavelet threshold noise reduction. The effectiveness of the method was proved in turbine tests, and the selection of the threshold had a direct impact on the noise reduction effect. The wavelet algorithm has been widely used in acoustic emission signal noise reduction and has achieved good results, but it also has some inherent defects, such as the effect of the wavelet based on noise reduction. [PERSON] et al. [16] pointed out that the denoising performance is affected by factors such as wavelet basis function, decomposition level, threshold method, and threshold selection criteria, whereas traditional parameter selection methods rely on statistics and experience. This team proposed a threshold denoising method based on a hybrid particle swarm optimization algorithm and compared five common denoising methods. The results show that this method is more effective. [PERSON] et al. [17] found that the wavelet noise reduction method using a single basis function had poor effects in the face of low signal-to-noise ratio signals with the same frequency interference. A noise reduction method based on EMD and wavelet packets was proposed to solve this problem, and its reliability is proved through welding crack experiments.