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Application of Empirical Mode Decomposition and Fuzzy Entropy to High-Speed Rail Fault Diagnosis

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Foundations of Intelligent Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 277))

Abstract

In this paper, a signal feature extraction technique for the fault diagnosis of high-speed rail, based on empirical mode decomposition and fuzzy entropy, is presented. The vibration signals collected from the train in different running condition were decomposed with empirical mode decomposition method into a number of intrinsic mode functions. After removing pseudo mode functions, all intrinsic mode functions instead of the first few intrinsic modes of functions are taken into consideration. We calculate the mean of fuzzy entropies of the all intrinsic mode functions as the feature of signal. The experiments show that the extracted feature can recognize fault patterns accurately and effectively.

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Acknowledgments

This study is funded by National Natural Science Foundation of China (61134002, 61170111, 61175047) and Research Funds of Traction Power State Key Laboratory of Southwest Jiaotong University(2012TPL_T15)

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Correspondence to Yan Yang .

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Zhao, J., Yang, Y., Li, T., Jin, W. (2014). Application of Empirical Mode Decomposition and Fuzzy Entropy to High-Speed Rail Fault Diagnosis. In: Wen, Z., Li, T. (eds) Foundations of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 277. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54924-3_9

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  • DOI: https://doi.org/10.1007/978-3-642-54924-3_9

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54923-6

  • Online ISBN: 978-3-642-54924-3

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