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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Banerjee TP, Das S, Roychoudhury J, Abraham A (2010) Implementation of a new hybrid methodology for fault signal classification using short-time fourier transform and support vector machines. Soft computing models in industrial and environmental applications. doi:10.1007/978-3-642-13161-5_28
Rosero J, Romeral JAO, Romeral L, Rosero E (2008) Short circuit fault detection in PMSM by means of empirical mode decomposition (EMD) and wigner ville distribution (WVD). Applied power electronics conference and exposition. doi:10.1109/APEC.2008.4522706
Saravanan N, Ramachandran KI (2010) Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN). Expert Syst Appl 37(6):4168–4181
Ai S, Li H (2008) Gear fault detection based on ensemble empirical mode decomposition and Hilbert-Huang transform. Fuzzy systems and knowledge discovery. doi:10.1109/FSKD.2008.64
Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R S A: Math Phys Eng Sci 454(1971):903–995
Shen Z, Chen X, Zhang X, He Z (2012) A novel intelligent gear fault diagnosis model based on EMD and multi-class TSVM. Measurement 45(1):30–40
He Y, Huang J, Zhang B (2012) Approximate entropy as a nonlinear feature parameter for fault diagnosis in rotating machinery. Meas Sci Technol 23(4):1–14
Ding JM, Lin JH, Yang Q (2010) Real-time diagnosis of bearing faults based on harmonic wavelet singular entropy. Chin Mech Eng 21:55–58
Lake DE, Richman JS, Griffin MP, Moorman JR (2002) Sample entropy analysis of neonatal heart rate variability. Am J Physiol Regul Integr Comp Physiol 283(3):R789–R797
Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci 88(6):2297–2301
Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278(6):H2039–H2049
Chen W, Wang Z, Xie H, Yu W (2007) Characterization of surface EMG signal based on fuzzy entropy. Neural Syst Rehabil Eng 15(2):266–272
Chen W, Zhuang J, Yu W, Wang Z (2009) Measuring complexity using FuzzyEn, ApEn, and SampEn. Med Eng Phys 31(1):61–68
Wang L, Ji G (2012) Fault diagnosis of rotor system based on EMD-fuzzy entropy and SVM. Noise Vib Control 32(3):171–176
Yadav SK, Kalra PK (2009) Fault diagnosis of internal combustion engine using empirical mode decomposition. Image and signal processing and analysis. pp 40–47
Shen Z, Chen X, Zhang X, He Z (2012) A novel intelligent gear fault diagnosis model based on EMD and multi-class TSVM. Measurement 45(1):30–40
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-642-54924-3_9
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54923-6
Online ISBN: 978-3-642-54924-3
eBook Packages: EngineeringEngineering (R0)