Fault diagnosis of hydraulic piston pumps based on a two-step EMD method and fuzzy C-means clustering

Hydraulic piston pumps are commonly used in aircrafts and various other equipment, and efficient fault diagnosis of them is playing an important role in improving the reliability and performance of hydraulic systems. Given that the discharge pressure signal of piston pump is a quasi-periodic signal and contains variety of state information, this article proposes a fault diagnosis method combining a two-step empirical mode decomposition (EMD) method based on waveform matching and extrema mirror extension with fuzzy C-means clustering. Based upon discharge pressure signals of piston pumps, the two-step EMD method which can restrain the end effects of traditional EMD is adopted to decompose the original signal. Characteristic vectors are then constructed by computing the normalized characteristic energy of selected Intrinsic Mode Function (IMF) components on the basis of local Hilbert marginal energy spectrum. Finally, fuzzy C-means clustering algorithm is used to identify the faults of pumps. Experimental results indicate that the proposed method can identify the faults of pumps effectively.

[1]  Kim Le,et al.  Fuzzy Relation Compositions and Pattern Recognition , 1996, Inf. Sci..

[2]  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.

[3]  Jiangping Wang,et al.  Vibration-based fault diagnosis of pump using fuzzy technique , 2006 .

[4]  David He,et al.  A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology , 2007 .

[5]  Da-Chao Lin,et al.  Elimination of end effects in empirical mode decomposition by mirror image coupled with support vector regression , 2012 .

[6]  Junsheng Cheng,et al.  Application of support vector regression machines to the processing of end effects of Hilbert Huang transform , 2007 .

[7]  Yu Yang,et al.  A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM , 2007 .

[8]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[9]  Anhua Chen,et al.  Degradation assessment and fault diagnosis for roller bearing based on AR model and fuzzy cluster analysis , 2011 .

[10]  Jun Du,et al.  Layered clustering multi-fault diagnosis for hydraulic piston pump , 2013 .

[11]  Yi Shen,et al.  Boundary extension for Hilbert-Huang transform inspired by gray prediction model , 2012, Signal Process..

[12]  Qin Zhang,et al.  WAVELET APPROACH FOR PERFORMANCE MONITORING AND DIAGNOSIS OF A HYDRAULIC PUMP , 2005 .

[13]  Michael Feldman,et al.  TOWARD HELICOPTER GEARBOX DIAGNOSTICS FROM A SMALL NUMBER OF EXAMPLES , 2000 .

[14]  Qin Zhang,et al.  WAVELET--BASED PRESSURE ANALYSIS FOR HYDRAULIC PUMP HEALTH DIAGNOSIS , 2003 .

[15]  Norden E. Huang,et al.  Derivative-optimized empirical mode decomposition for the Hilbert-Huang transform , 2014, J. Comput. Appl. Math..

[16]  Gabriel Rilling,et al.  On empirical mode decomposition and its algorithms , 2003 .

[17]  Dejie Yu,et al.  Application of frequency family separation method based upon EMD and local Hilbert energy spectrum method to gear fault diagnosis , 2008 .

[18]  Zhen Zhao,et al.  Intermittent chaos and sliding window symbol sequence statistics-based early fault diagnosis for hydraulic pump on hydraulic tube tester , 2009 .

[19]  Qin Zhang,et al.  A Wavelet Packet and Residual Analysis Based Method for Hydraulic Pump Health Diagnosis , 2006 .

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

[21]  Ram Bilas Pachori,et al.  Variable cosine windowing of intrinsic mode functions: Application to gear fault diagnosis , 2012 .

[22]  Hong Fan,et al.  Rotating machine fault diagnosis using empirical mode decomposition , 2008 .

[23]  Paolo Pennacchi,et al.  Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions , 2011 .

[24]  V. Rai,et al.  Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform , 2007 .

[25]  Jin Chen,et al.  Bearing performance degradation assessment based on lifting wavelet packet decomposition and fuzzy c-means , 2010 .

[26]  Lin Tingqi,et al.  Fault diagnosis of airplane hydraulic pump , 2002, Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527).

[27]  Qi Ren-jun.  Mechanism Research of Pressure Ripple for Hydraulic Systems , 2001 .

[28]  Yan Zhang,et al.  Assessment of arterial distension based on continuous wave Doppler ultrasound with an improved Hilbert-Huang processing , 2010, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[29]  Jihong Yan,et al.  Improved Hilbert-Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis , 2014, Signal Process..

[30]  Liangsheng Qu,et al.  An improved method for restraining the end effect in empirical mode decomposition and its applications to the fault diagnosis of large rotating machinery , 2008 .

[31]  Balbir S. Dhillon,et al.  Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network , 2012 .

[32]  D N Johnston,et al.  The ‘Secondary Source’ Method for the Measurement of Pump Pressure Ripple Characteristics Part 1: Description of Method , 1990 .

[33]  J. C. Hung,et al.  Pulsating parameter method for fault diagnosis for a hydraulic pump , 1991, Proceedings IECON '91: 1991 International Conference on Industrial Electronics, Control and Instrumentation.