CAJ | 학술논문

利用金属磁记忆法对煤矿系统中的钢绳芯输送带进行早期故障诊断时，发现信号中包含的噪声对诊断结果影响较大。针对此问题，提出了基于总体平均经验模态分解和改进小波半软阈值的降噪算法。首先利用总体平均经验模态分解得到若干个本征模函数，经过相关性分析后提取本征模函数的有效分量，用人工蜂群优化算法改进阈值函数，再分别对有效分量进行改进的小波半软阈值函数降噪处理，最后将信号重构。经过降噪处理后的磁记忆信号能较好地保留信号中的有用信息。仿真实验结果表明，该算法可实现噪声环境下的钢绳芯输送带应力集中区特征的有效提取，从而实现早期故障诊断。
이용금속자기억법대매광계통중적강승심수송대진행조기고장진단시，발현신호중포함적조성대진단결과영향교대。침대차문제，제출료기우총체평균경험모태분해화개진소파반연역치적강조산법。수선이용총체평균경험모태분해득도약간개본정모함수，경과상관성분석후제취본정모함수적유효분량，용인공봉군우화산법개진역치함수，재분별대유효분량진행개진적소파반연역치함수강조처리，최후장신호중구。경과강조처리후적자기억신호능교호지보류신호중적유용신식。방진실험결과표명，해산법가실현조성배경하적강승심수송대응력집중구특정적유효제취，종이실현조기고장진단。
When using metal magnetic memory method to diagnose the early faults of steel‐cord belt ,we found that the noise that comes along in the signal ,to a great extent ,affects the re‐sults of the diagnosis .Aiming at overcoming the deficiency ,we proposed a noise decomposition algorithm based on ensemble empirical mode decomposition (EEMD ) and improved semi‐soft wavelet threshold .The algorithm first decomposes the empirical mode to obtain several intrinsic mode functions ,and extracts valid components through Intrinsic Mode Function (IMF)correlation analysis .For each of the components ,the improved semi‐soft wavelet threshold function is used to reduce the noise and reconstruct the signal ,thus enhancing the ability of metal magnetic memory signals after the noise reduction to keep useful information ,which is collected from original signal in an effective manner .The results of simulation experiment show that the algorithm can effec‐tively extract thecharacteristics from steel‐cord belt stress concentration in noisy environments and thereby enable early faults to be diagnosed .