Manifold Learning with Self-Organizing Mapping for Feature Extraction of Nonlinear Faults in Rotating Machinery
Manifold Learning with Self-Organizing Mapping for Feature Extraction of Nonlinear Faults in Rotating Machinery
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DOI：
10.1155/2015/873905
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发表时间：
2015-10
影响因子：
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通讯作者：
Lin Liang;Fei Liu;Maolin Li;Guanghua Xu
中科院分区：
文献类型：
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作者：
Lin Liang;Fei Liu;Maolin Li;Guanghua Xu
提出了一种利用自组织映射流形自动提取低维特征的新方法，用于旋转机械非线性故障（如碰擦、底座松动等）的检测。在单个振动信号重构的相空间下，采用期望最大化迭代算法的自组织映射（SOM）自适应地划分局部邻域，无需人工干预。之后，采用局部切空间对齐算法将高维相空间压缩到低维特征空间。该方法利用了SOM低维特征提取和自适应邻域构建中的流形学习的优点，可以在二维投影空间中提取感兴趣的内在故障特征。为了评估所提出方法的性能，对洛伦兹系统进行了仿真，并获得了具有非线性故障的旋转机械用于测试目的。与全息谱方法相比，结果表明，该方法在故障识别方面具有优越性，并且可以有效地用于旋转机械状态监测。
A new method for extracting the low-dimensional feature automatically with self-organization mapping manifold is proposed for the detection of rotating mechanical nonlinear faults (such as rubbing, pedestal looseness). Under the phase space reconstructed by single vibration signal, the self-organization mapping (SOM) with expectation maximization iteration algorithm is used to divide the local neighborhoods adaptively without manual intervention. After that, the local tangent space alignment algorithm is adopted to compress the high-dimensional phase space into low-dimensional feature space. The proposed method takes advantages of the manifold learning in low-dimensional feature extraction and adaptive neighborhood construction of SOM and can extract intrinsic fault features of interest in two dimensional projection space. To evaluate the performance of the proposed method, the Lorenz system was simulated and rotation machinery with nonlinear faults was obtained for test purposes. Compared with the holospectrum approaches, the results reveal that the proposed method is superior in identifying faults and effective for rotating machinery condition monitoring.