A mechanical fault characteristic extraction method based on orthogonal non-negative matrix factorization

机译：基于正交非负矩阵分解的机械故障特征提取方法

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A new characteristic extracting approach based on orthogonality non-negative matrix factorization with principle component analysis solving the initialization problem (PCAONMF) is presented. The research for this algorithm is added the orthogonality constraints to increase the orthogonality, locality and independence of the basic vectors, and make the factorization results easy to explain. On the other hand, the initialization of principle component analysis improves the robustness of the algorithm. Simulation experiments show that the characteriatic basis vectors based on PCA-ONMF have smaller reconstruction error. And the gear box fault recognition experiment results with higher recognition accuracy confirm the effectiveness of the method.

机译：提出了一种基于正交非负矩阵分解的主成分分析特征提取方法，解决了初始化问题。该算法的研究增加了正交性约束，以增加基本矢量的正交性，局部性和独立性，并使分解结果易于解释。另一方面，主成分分析的初始化提高了算法的鲁棒性。仿真实验表明，基于PCA-ONMF的特征基向量具有较小的重构误差。齿轮箱故障识别实验结果具有较高的识别精度，证明了该方法的有效性。

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《2010 International Conference on Mechanic Automation and Control Engineering》|2010年|P.5657-5660|共4页
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作者
Ji Jin-lu; Xu Fei-yun;
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正文语种
中图分类自动化技术、计算机技术;
关键词
characteristic extraction; non-negative matrix factorization; orthogonality; principle component analysis;

机译：特征提取非负矩阵分解正交性原理成分分析;

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A mechanical fault characteristic extraction method based on orthogonal non-negative matrix factorization

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