农业工程学报
農業工程學報
농업공정학보
2014年
23期
70-78
,共9页
王志坚%韩振南%刘邱祖%宁少慧
王誌堅%韓振南%劉邱祖%寧少慧
왕지견%한진남%류구조%저소혜
轴承%故障检测%信号分析%齿轮箱%最小熵反褶积%总体平均经验模态分解%微弱故障%多故障
軸承%故障檢測%信號分析%齒輪箱%最小熵反褶積%總體平均經驗模態分解%微弱故障%多故障
축승%고장검측%신호분석%치륜상%최소적반습적%총체평균경험모태분해%미약고장%다고장
bearing%fault detection%signal analysis%gearbox%minimum entropy deconvolution%ensemble empirical mode decomposition%weak fault%multi-fault
针对滚动轴承在强噪声环境下故障信号微弱、故障特征难以提取等问题,提出了基于最小熵反褶积(minimum entropy deconvolution,MED)和总体平均经验模态分解(ensemble empirical mode decomposition, EEMD)两者相结合的方法来提取滚动轴承微弱故障特征。通过对仿真信号和风电齿轮箱的振动信号分析,结果表明:为了弥补在强背景噪声下EEMD对微弱信号特征提取的局限性,该文选取MED作为EEMD的前置滤波器,最后对敏感的本征模态函数进行循环自相关函数解调分析,得出了风电齿轮箱的故障来自于高速轴的微小弯曲和高速轴输出端#10轴承外圈点蚀。同时与EEMD进行对比分析,表明了这种方法对微弱故障特征提取有较好的适用性。该文为多故障共存并处于强背景噪声下的微弱特征提取提供了参考。
針對滾動軸承在彊譟聲環境下故障信號微弱、故障特徵難以提取等問題,提齣瞭基于最小熵反褶積(minimum entropy deconvolution,MED)和總體平均經驗模態分解(ensemble empirical mode decomposition, EEMD)兩者相結閤的方法來提取滾動軸承微弱故障特徵。通過對倣真信號和風電齒輪箱的振動信號分析,結果錶明:為瞭瀰補在彊揹景譟聲下EEMD對微弱信號特徵提取的跼限性,該文選取MED作為EEMD的前置濾波器,最後對敏感的本徵模態函數進行循環自相關函數解調分析,得齣瞭風電齒輪箱的故障來自于高速軸的微小彎麯和高速軸輸齣耑#10軸承外圈點蝕。同時與EEMD進行對比分析,錶明瞭這種方法對微弱故障特徵提取有較好的適用性。該文為多故障共存併處于彊揹景譟聲下的微弱特徵提取提供瞭參攷。
침대곤동축승재강조성배경하고장신호미약、고장특정난이제취등문제,제출료기우최소적반습적(minimum entropy deconvolution,MED)화총체평균경험모태분해(ensemble empirical mode decomposition, EEMD)량자상결합적방법래제취곤동축승미약고장특정。통과대방진신호화풍전치륜상적진동신호분석,결과표명:위료미보재강배경조성하EEMD대미약신호특정제취적국한성,해문선취MED작위EEMD적전치려파기,최후대민감적본정모태함수진행순배자상관함수해조분석,득출료풍전치륜상적고장래자우고속축적미소만곡화고속축수출단#10축승외권점식。동시여EEMD진행대비분석,표명료저충방법대미약고장특정제취유교호적괄용성。해문위다고장공존병처우강배경조성하적미약특정제취제공료삼고。
Under the complex environment, rotating machinery such as wind turbine gearboxes has multi-gearing and multi-bearing. The dynamic responses of these components are complex and interfering with each other. It is usually difficult to diagnose their potential faults. Especially when multiple faults and strong background noise coexist, vibration signals excited by several faults are combined with each other nonlinearly and non-stationary, which makes the observed vibration signals rather complex and difficult to identify each fault by using traditional methods. Especially the rolling bearing’s fault feature under strong background noise is very weak and usually overwhelmed by noise. In this paper, minimum entropy deconvolution (MED) and ensemble empirical mode decomposition(EEMD) were combined for rolling bearing’s weak fault diagnosis. EEMD is a self-adaptive analysis method and can decompose a complicated signal into a series of intrinsic mode functions (IMFs) according to the signal’s local characteristics. EEMD is more accurate and effective for diagnosing the faults of rotating machinery than MED, so it has been used in the fault feature extraction of rolling bearing. However, if the frequency components in the signal are too complex and the background noise is very strong, they will affect the decomposition result, therefore it is very important to improve the signal-to-noise ratio of the original signal. MED searches for an optimum set of filter coefficients that can recover the output signal with the maximum value of kurtosis, which is an indicator that reflects the peak of a signal, so MED technique aims to extract the fault impulses while minimizing the noise and therefore resulting in clear detection results even under high noise, which can remedy the shortage of EEMD. In this paper, MED and EEMD were combined for the simulation signal and the wind turbine gearbox. Firstly, MED was used as the pre-filter to refine the vibration signal, and the strong background noise of rolling bearing was decreased by the MED method. Secondly, the given signal above was processed by the EEMD, and the amplitude of the added white noise could be determined through a trial-and-error method, which could be implemented based on minimizing the problems of mode aliasing. At last the sensitive intrinsic mode functions (IMFs) were analyzed by cyclic autocorrelation function (CAF), which could be applied to separate out the modulators effectively, especially for the weak modulators due to fault effects that could not be detected by other conventional technologies. We analyzed the wind turbine gearbox by combining CAF with EEMD, when the amplitude of the added white noise was 0.4 and the number of the ensemble was 100, the components 28 and 302 Hz corresponded to twice rotational frequency of high speed shaft and fault frequency of outer ring of #10 bearing. The result showed that high-speed shaft of wind turbine gearbox was slightly bent and there was slight pit on the face of outer rings of #10 bearings. The analyzed results demonstrate that the proposed method is an effective approach in identifying weak fault feature under strong background noise of rotating machinery.