摘要
目前对齿轮裂纹的诊断研究多采用定性诊断,而工程实际中往往更关注定量诊断。由于齿轮裂纹信号往往表现出非线性非平稳特征,处理这类信号通常采用时频分析。自适应最稀疏时频分析(Adaptive and Sparsest TimeFrequency Analysis,简称ASTFA)是一种新的时频分析方法,相比于经验模态分解(Empirical Mode Decomposition,简称EMD)方法,ASTFA方法能更好地抑制端点效应和模态混淆,但ASTFA方法也存在分解得到的分量排列不规律的缺陷,从而给特征提取时分量的选择带来困难。针对这一问题,提出了一种改进ASTFA算法,即基于主模态分析(Principle Mode Analysis,简称PMA)的自适应最稀疏时频分析(PMA-ASTFA)方法,该方法可以根据所选择的故障特征参数(一个或多个)对内禀模态函数(Intrinsic Mode Function,简称IMF)分量进行排序。根据齿轮故障实验台建立齿轮动力学模型,选择对齿轮裂纹敏感的故障特征参数,再把PMA-ASTFA方法用于实测的齿轮裂纹故障信号处理。实验信号的分析结果表明,提出的方法可以有效地实现齿轮裂纹故障的定量诊断。
Qualitative diagnosis methods is presently mostly adopted in gear tooth crack diagnose study, while quantitative diag-nosis methods are usually paid more attention in engineering practice. For the nonlinear and non-stationary features in the gear tooth crack signal, the time-frequency analysis method is usually applied to deal with this kind of signal. The Adaptive and Sparsest Time-Frequency Analysis （ASTFA） is a new method of time-frequency analysis. Compared with Empirical Mode De-composition （EMD） method, ASTFA method has the advantage in reducing mode mixing and end effect. However, irregular component arrangement defect appears in ASTFA method, making it difficult to select components during feature extraction. In order to solve this problem, an improved ASTFA method is proposed in this paper, which is the Principle Mode Analysis based ASTFA method （PMA-ASTFA）. The PMA-ASTFA method can reorder the Intrinsic Mode Functions （IMFs） accord-ing to fault eigenvalues （one or more）. For purpose of screening out fault eigenvalues which are sensitive to gear tooth cracks, a gear dynamic model based on gear fault test bench is established in this paper. Then the PMA-ASTFA method is used to process the measured gear tooth crack signals. The result of experimental signal analysis shows that the proposed method can realize quantitative diagnosis of gear tooth cracks effectively.
出处
《振动工程学报》
EI
CSCD
北大核心
2017年第5期849-855,共7页
Journal of Vibration Engineering
基金
国家重点研发计划项目(2016YFF0203400)
国家自然科学基金资助项目(51575168
51375152)
智能型新能源汽车国家2011协同创新中心
湖南省绿色汽车2011协同创新中心资助项目
关键词
故障诊断
改进的自适应最稀疏时频分析
主模态分析
齿轮裂纹
定量诊断
fault diagnosis
improved adaptive and sparsest time-frequency analysis
principle mode analysis
gear tooth crack
quantitative diagnosis
