Shijin Ren
School of Computer Science and Technology, Jiangsu Xuzhou Normal University, Xuzhou, Jiangsu, 221116, China
Ping Ling
School of Computer Science and Technology, Jiangsu Xuzhou Normal University, Xuzhou, Jiangsu, 221116, China
Maoyun Yang
School of Computer Science and Technology, Jiangsu Xuzhou Normal University, Xuzhou, Jiangsu, 221116, China
Yinlong Ni
School of Computer Science and Technology, Jiangsu Xuzhou Normal University, Xuzhou, Jiangsu, 221116, China
Zhihuan Song
National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
ABSTRACT
Dimensional reduction is crucial to machine condition monitoring and diagnosis since the extracted features are often redundant and heterogeneous as well as high dimensional data often are embedded in lower-dimensional manifold. Inspired by manifold learning and multiple-kernel learning theory, multi-kernel principal component analysis with discriminant manifold (DMMKPCA) is proposed for host fault monitoring and diagnosis. The method not only preserves the local and global structures of data set but also handles heterogeneous characteristic sets, which inherits the excellences of LPMVP and multiple kernel learning. A two-stage iterative optimization algorithm is proposed to obtain the optimal combining weights of multiple kernel functions and parameters of each kernel functions. A case study of hoist illustrates the efficiency of the proposed algorithm on the information extraction and fault detection.
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How to cite this article
Shijin Ren, Ping Ling, Maoyun Yang, Yinlong Ni and Zhihuan Song, 2013. Multi-Kernel PCA with Discriminant Manifold for Hoist Monitoring. Journal of Applied Sciences, 13: 4195-4200.
DOI: 10.3923/jas.2013.4195.4200
URL: https://scialert.net/abstract/?doi=jas.2013.4195.4200
DOI: 10.3923/jas.2013.4195.4200
URL: https://scialert.net/abstract/?doi=jas.2013.4195.4200
REFERENCES
- Ghoraani, B. and S. Krishnan, 2011. Time-frequency matrix feature extraction and classification of environmental audio signals. IEEE Trans. Audio Speech Language Process., 19: 2197-2210.
CrossRef - He, Q., 2013. Time-frequency manifold for nonlinear feature extraction in machinery fault diagnosis. Mech. Syst. Signal Process., 35: 200-218.
CrossRefDirect Link - Jiang, Q., M. Jia, J. Hu and F. Xu, 2009. Machinery fault diagnosis using supervised manifold learning. Mech. Syst. Signal Process., 23: 2301-2311.
CrossRefDirect Link - Lin, Y.Y., T.L. Liu and C.S. Fuh, 2011. Multiple kernel learning for dimensionality reduction. IEEE Trans. Pattern Anal. Machine Intell., 33: 1147-1160.
CrossRefDirect Link - Liu, L., W. Zuo, D. Zhang, N. Li and H. Zhang, 2012. Combination of heterogeneous features for wrist pulse blood flow signal diagnosis via multiple kernel learning. IEEE Trans. Inform. Technol. Biomedicine, 16: 598-606.
CrossRefDirect Link - Wang, S., W. Huang and Z.K. Zhu, 2011. Transient modeling and parameter identification based on wavelet and correlation filtering for rotating machine fault diagnosis. Mech. Syst. Signal Process., 25: 1299-1320.
CrossRefDirect Link - Wang, Z., S.C. Chen and T. Sun, 2008. MultiK-MHKS: A novel multiple kernel learning algorithm. IEEE Trans. Pattern Anal. Mach. Intell., 30: 348-353.
CrossRefDirect Link - Xie, X. and H. Shi, 2012. Dynamic multimode process modeling and monitoring using adaptive gaussian mixture models. Ind. Eng. Chem. Res., 51: 5497-5505.
CrossRefDirect Link - Yu, J., 2012. Semiconductor manufacturing process monitoring using gaussian mixture model and bayesian method with local and nonlocal information. IEEE Trans. Semiconduct. Manuf., 25: 480-493.
CrossRefDirect Link - Lei, Y., Z. He and Y. Zi, 2011. EEMD method and WNN for fault diagnosis of locomotive roller bearings. Expert Syst. Appl., 38: 7334-7341.
CrossRefDirect Link