A new data mining approach for gear crack level identification based on manifold learning
A new data mining approach for gear crack level identification based on manifold learning
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基于流形学习的齿轮裂纹级别识别数据挖掘新方法
DOI：
10.5755/j01.mech.18.1.1276
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发表时间：
2012-01-01
期刊：
影响因子：
0.7
通讯作者：
Wu, Jingping
中科院分区：
文献类型：
--
作者：
Li, Zhixiong;Yan, Xinping;Wu, Jingping
齿轮裂纹是齿轮机构中常见的一种损伤模式，意外的严重裂纹可能导致传动系统瘫痪，造成重大的经济损失。因此，有效的早期故障检测和诊断对机械的正常运行至关重要。故障诊断的关键之一是特征的提取和选择。文献综述表明，在机械故障诊断领域，利用流形学习算法考虑特征空间的非线性特性的研究有限，用于齿轮裂纹检测的非线性特征提取较少。提出了一种基于经验模态分解（EMD）和监督局部线性嵌入（SLLE）的数据挖掘方法，并将其应用于齿轮裂纹水平识别。利用EMD将振动信号分解为若干内禀模态函数（IMFs）进行特征提取，利用SLLE进行非线性特征选择。利用该方法对齿轮故障试验台采集的振动数据进行特征约简和提取。研究结果表明，EMD-SLLE可以有效地揭示齿轮不同裂纹严重程度振动信号之间的敏感特性。随着齿轮运行工况的变化，IMFs的能量分布和统计特征也会发生变化，通过非线性SLLE方法可以提取出最显著的特征。此外，SLLE的特征提取性能优于线性主成分分析（PCA）方法。
Gear crack is a common damage model in the gear mechanisms, and an unexpected serious crack may break the transmission system down, leading to significant economic losses. Efficient incipient fault detection and diagnosis are therefore critical to machinery normal running. One of the key points of the fault diagnosis is feature extraction and selection. Literature review indicates that only limited research considered the nonlinear property of the feature space by the use of manifold learning algorithms in the field of mechanic fault diagnosis, and nonlinear feature extraction for gear crack detection are scarce. This paper reports a novel data mining method based on the empirical mode decomposition (EMD) and supervised locally linear embedding (SLLE) applied to gear crack level identification. The EMD was used to decompose the vibration signals into a number of intrinsic mode functions (IMFs) for feature extraction, whilst the SLLE for nonlinear feature selection. The experimental vibration data acquired from the gear fault test-bed were processed for feature reduction and extraction using the proposed method. Study results show that the sensitive characteristics between different gear crack severity vibration signals can be revealed effectively by EMD-SLLE. The energy distribution and the statistic features of IMFs vary with the change of the gear operation conditions, and the most distinguished features can be extracted by nonlinear method of SLLE. In addition, the performance of feature extraction of SLLE is better than that of the linear method of principal component analysis (PCA).