本文選題：不同工況　切入點：降噪　出處：《太陽能學報》2017年09期 　論文類型：期刊論文

【摘要】：針對不同恒定工況下的齒輪微弱故障難于診斷的問題,提出一種局部特征尺度分解(local characteristics-cale decomposition,LCD)結合全矢互信息的故障診斷方法。采用LCD對不同工況的振動信號進行分解,獲取瞬時頻率具有物理意義的各階內稟尺度分量(intrinsic scale component,ISC),可消除工況所引起的頻率調制及模態(tài)混疊效應所造成的干擾,再以ISC與原信號的互相關系數(shù)最大為準則進一步實現(xiàn)降噪。提取不同工況下的樣本信號與降噪后ISC的全矢互信息絕對值之和作為樣本特征向量,使用支持向量機進行分類。通過對不同工況的100組信號的識別,表明該方法能有效區(qū)分不同工況下的齒輪微弱故障特征,同時減少對人的主觀經驗的依賴。
[Abstract]:In order to solve the problem that it is difficult to diagnose the weak faults of gears under different constant working conditions, a local characteristic scale decomposition (characteristics-cale decompositionLCD) method combined with full vector mutual information is proposed. The vibration signals under different working conditions are decomposed by LCD. The intrinsic scale component of the intrinsic scale components of each order of the instantaneous frequency with physical significance can eliminate the interference caused by frequency modulation and modal aliasing. Then the maximum correlation number between the ISC and the original signal is taken as the criterion for further noise reduction. The sum of the absolute values of the sample signal and the total vector mutual information of the de-noised ISC under different working conditions is extracted as the sample feature vector. By using support vector machine (SVM) to classify 100 sets of signals under different working conditions, it is shown that this method can effectively distinguish the weak fault characteristics of gears under different working conditions and reduce the dependence on human subjective experience.
【作者單位】： 新疆大學機械工程學院;西安交通大學機械工程學院;
【基金】：國家自然科學基金(51565055) 新疆維吾爾自治區(qū)研究生科研創(chuàng)新項目(XJGRI2014025)
【分類號】：TH132.41
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本文編號：1651842