Abstract
Aiming at the uncertainty of fault diagnosis in turbine generator based on single fault information, a diagnosis system of multi-sensors fusion characteristics based on kernel principle component analysis (KPCA) and mutative scale chaos combined clustering is proposed. Firstly, the vibration features of generator’s stator and rotor and the circulation features of stator winding parallel branch were combined and then with which used KPCA to carry out dimensionality reduction fusion, obtained complementary features and selected nonlinear principal components as fault analysis data, finally used the mutative scale chaos optimization algorithms (MSCOA)-FCM algorithm to realize fault identification. The fault diagnosis example of generator shows the validity and practicability of the algorithm; compared to a single signal source, it has a considerable improvement in the accuracy of fault diagnosis and is more suitable for fault identification.
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Acknowledgments
This work is supported by National Science Foundation of China (No. 51107039).
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Wu, Y., Zhao, Q., Feng, W. (2014). Fault Diagnosis Method of Generator Based on Mutative Scale Chaos Combined Clustering and Feature Fusion. In: Jia, L., Liu, Z., Qin, Y., Zhao, M., Diao, L. (eds) Proceedings of the 2013 International Conference on Electrical and Information Technologies for Rail Transportation (EITRT2013)-Volume I. Lecture Notes in Electrical Engineering, vol 287. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53778-3_12
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DOI: https://doi.org/10.1007/978-3-642-53778-3_12
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