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机构地区:[1]College of Electronic Information Engineering,Inner Mongolia University,Hohhot,China
出 处:《International Journal of Mechanical System Dynamics》2024年第4期472-486,共15页国际机械系统动力学学报(英文)
基 金:Inner Mongolia Autonomous Region Science and Technology Major Special Project,Grant/Award Number:2021ZD0019-4。
摘 要:To address the difficulty in extracting early fault feature signals of rolling bearings,this paper proposes a novel weak fault diagnosis method for rolling bearings.This method combines the Improved Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise(ICEEMDAN)and the Improved Maximum Correlated Kurtosis Deconvolution(IMCKD).Utilizing the kurtosis criterion,the intrinsic mode functions obtained through ICEEMDAN are reconstructed and denoised using IMCKD,which significantly reduces noise in the measured signal.This approach maximizes the energy amplitude at the fault characteristic frequency,facilitating fault feature identification.Experimental studies on two test benches demonstrate that this method effectively reduces noise interference and highlights the fault frequency components.Compared with traditional methods,it significantly improves the signal-to-noise ratio and more accurately identifies fault features,meeting the requirements for discriminating rolling bearing faults.The method proposed in this study was applied to the measured vibration signals of the gearbox bearings in the new high-speed wire department of a Long Products Mill.It successfully extracted weak characteristic information of early bearing faults,achieving the expected diagnostic results.This further validates the effectiveness of the ICEEMDAN–IMCKD method in practical engineering applications,demonstrating significant engineering value for detecting and extracting weak impact characteristics in rolling bearings.
关 键 词:rolling bearings early fault intrinsic mode functions Improved Complementary Ensemble Empirical Mode Decomposition Improved Maximum Correlated Kurtosis Deconvolution
分 类 号:P31[天文地球—固体地球物理学]
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