Early fault detection based on empirical mode decomposition method

Abstract Vibration signal analysis is a widely used condition monitoring technique. Though the extraction of information from the raw vibration signals is difficult because of its non-stationary and non-linear nature, the Empirical mode decomposition has proven to be an effective method. In this method, the raw vibration signal is decomposed into the various intrinsic mode functions. Further, the energy content of these intrinsic modes and the spectral entropy’s associated with each intrinsic mode is analyzed. The method is validated with the available data sets. Based on the results obtained, this method has proven to work better for early fault detection.

[1]  E. Peter Carden,et al.  Vibration Based Condition Monitoring: A Review , 2004 .

[2]  Zhongxiao Peng,et al.  A study of the effect of contaminant particles in lubricants using wear debris and vibration condition monitoring techniques , 2005 .

[3]  Sanjay H Upadhyay,et al.  A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings , 2016 .

[4]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[5]  Marc Lavielle,et al.  Using penalized contrasts for the change-point problem , 2005, Signal Process..

[6]  Yaguo Lei,et al.  EEMD method and WNN for fault diagnosis of locomotive roller bearings , 2011, Expert Syst. Appl..

[7]  Manuel Duarte Ortigueira,et al.  On the HHT, its problems, and some solutions , 2008 .

[8]  Mayorkinos Papaelias,et al.  Condition monitoring of wind turbines: Techniques and methods , 2012 .

[9]  Buyung Kosasih,et al.  Acoustic emission-based condition monitoring methods: Review and application for low speed slew bearing , 2016 .

[10]  Zhijing Yang,et al.  A method to eliminate riding waves appearing in the empirical AM/FM demodulation , 2008, Digit. Signal Process..

[11]  Yaguo Lei,et al.  A review on empirical mode decomposition in fault diagnosis of rotating machinery , 2013 .

[12]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..