Study on Vibration Signal Processing of Dense Medium Cyclone based on CEEMDAN-SampEn-Improved Wavelet Threshold

LI ShunTang; ZHENG Gangfeng; ZHANG Hailong; WU Ziyang; ZHOU Zheng

doi:10.16447/j.cnki.cpt.2025.02.011

Abstract：To effectively eliminate external noise from the vibration signals of dense medium cyclones and en-hance the efficiency and reliability of fault detection,a novel signal processing method combining Complete En-semble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN),Sample Entropy(SampEn),and an improved wavelet threshold technique is proposed.This method addresses the challenges of poor noise reduc-tion and difficulty in feature extraction from dense medium cyclone vibration signals.Firstly,CEEMDAN is em-ployed to suppress mode mixing and generate Intrinsic Mode Functions(IMFs).Subsequently,Sample Entropy is used to identify and filter noise components with higher entropy values.The selected noisy components are then processed using an improved wavelet threshold to remove noise while preserving effective signal features.Finally,the denoised IMF components are reconstructed along with the noise-free modal components to obtain an optimized signal.Simulation experiments demonstrate that the proposed method significantly outperforms traditional wavelet thresholding,CEEMDAN,and the combined CEEMDAN-Wavelet threshold method in terms of Signal-to-Noise Ratio(SNR),Mean Squared Error(MSE),and Normalized Cross-Correlation Coeffi-cient(NCC).Specifically,the SNR increases to 33.36 dB,the MSE decreases to 0.009 5,and the NCC reaches 0.999 9.In a practical validation,the vibration signal processing results from the Dense Medium Cyclone at the Wobei Coal Preparation Plant indicate that the first six IMF components,as identified by Sample Entropy,are predominantly noise.The proposed method effectively removes high-frequency noise while retaining low-fre-quency fault characteristics,demonstrating superior denoising performance and feature preservation in practical applications.Additionally,the Lempel-Ziv Complexity(LZC)is restored to 47.The proposed CEEM-DAN-SampEn-Improved Wavelet Threshold method achieves efficient noise reduction of dense medium cyc-lone vibration signals through multi-scale decomposition,entropy-based filtering,and optimized threshold de-noising.It provides clearer signals for fault diagnosis and has demonstrated excellent performance in both simu-lation and practical validation,highlighting its significant theoretical value and engineering application potential.