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World Academy of Science, Engineering and Technology
Published August 22, 2008 | Version 1704
Journal article Open

An Intelligent Combined Method Based on Power Spectral Density, Decision Trees and Fuzzy Logic for Hydraulic Pumps Fault Diagnosis

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Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. The aim of this work is to investigate the effectiveness of a new fault diagnosis method based on power spectral density (PSD) of vibration signals in combination with decision trees and fuzzy inference system (FIS). To this end, a series of studies was conducted on an external gear hydraulic pump. After a test under normal condition, a number of different machine defect conditions were introduced for three working levels of pump speed (1000, 1500, and 2000 rpm), corresponding to (i) Journal-bearing with inner face wear (BIFW), (ii) Gear with tooth face wear (GTFW), and (iii) Journal-bearing with inner face wear plus Gear with tooth face wear (B&GW). The features of PSD values of vibration signal were extracted using descriptive statistical parameters. J48 algorithm is used as a feature selection procedure to select pertinent features from data set. The output of J48 algorithm was employed to produce the crisp if-then rule and membership function sets. The structure of FIS classifier was then defined based on the crisp sets. In order to evaluate the proposed PSD-J48-FIS model, the data sets obtained from vibration signals of the pump were used. Results showed that the total classification accuracy for 1000, 1500, and 2000 rpm conditions were 96.42%, 100%, and 96.42% respectively. The results indicate that the combined PSD-J48-FIS model has the potential for fault diagnosis of hydraulic pumps.

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  • Z. K Peng, and F. L. Chu, "Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography," Mechanical Systems and Signal Processing, vol. 18, pp. 199-221. 2004.
  • H. Zheng, Z. Li, and X. Chen, "Gear fault diagnosis based on continuous wavelet transform," Mechanical systems and Signal Processing, vol. 16 (2-3), pp. 447-457. 2002.
  • R. F. M. Marcal, M. Negreiros, A. A. Susin, and J. L. Kovaleski, "Detecting faults in rotating machines," IEEE Instrumentation & Measurement Magazine, vol. 3 (4), pp 24-26. 2000.
  • P.A. Laggan, "Vibration monitoring," Proc. IEE Colloquium on Understanding your Condition Monitoring, pp. 1-11. 1999.
  • S. Pöyhönen, P. Jover, and H. Hyötyniemi, "Independent component analysis of vibration for fault diagnosis of an induction motor," in Proc. of the IASTED International Conference on Circuits, Signals, and Systems (CSS), Mexico, 2003, vol. 1, pp. 203-208.
  • B. Liu, and S. F. Ling, "On the selection of informative wavelets for machinery diagnosis," Mechanical Systems and Signal Processing, vol. 13, pp. 145-162. 1999.
  • H. Matuyama, "Diagnosis Algorithm," Journal of JSPE, vol. 75, pp. 35-37. 1991.
  • Q. B. Zhu, "Gear fault diagnosis system based on wavelet neural networks," Dynamics of Continuous Discrete and Impulsive Systemsseries A-Mathematical Analysis, vol. 13, pp. 671-673. 2006.
  • L. Jing, and Q. Liangsheng, "Feature extraction based on morlet wavelet and its application for mechanical fault diagnosis," Sound and Vibration, vol. 234, pp. 135-148. 2000. [10] J. P. Wang, and H. Hu, "Vibration-based fault diagnosis of pump using fuzzy technique," Measurement, vol. 39, pp. 176-185. 2006. [11] W.J. Wang, and P.D. McFadden, "Application of wavelets to gearbox vibration signals for fault detection," Sound and Vibration, vol. 192, pp. 927-939. 1996. [12] F. A. Andrade, I. Esat, and M. N. M. Badi, "A new approach to timedomain vibration condition monitoring: gear tooth fatigue crack detection and identification by the Kolmogorov-Smirnov test," Sound and Vibration, vol. 240. pp. 909-919. 2001. [13] N. Baydar, and A. Ball, "A Comparative study of acoustic and vibration signals in detection of gear failures using Wigner-Ville distribution," Mechanical Systems and Signal Processing, vol. 15, pp. 1091-1107. 2001. [14] M. A. Rao, J. Srinivas, V. B. V. Rama Raju, and K. V. S. Kumar, "Coupled torsional-lateral vibration analysis of geared shaft systems using mode analysis," Sound and Vibration, vol. 261, pp. 359-364. 2003. [15] B. Liu, "Adaptive harmonic wavelet transform with applications in vibration analysis," Sound and Vibration, vol. 262, pp. 45-64. 2003. [16] A. C. McCormick, A. K. Nandi, and L. B. Jack, "Application of periodic time-varying autoregressive models to the detection of earing faults," in Proc. of Institution of Mechanical Engineers, Part C: J. Mech. Eng. Sci, 1998, vol.. 212, pp. 417-428. [17] D. Ho, and R. B. Randall, "Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals," Mechanical System Signal Process, vol. 14, pp. 763-788. 2000. [18] J. Antoni, R. B. "Randall, Differential diagnosis of gear and bearing faults," Trans. ASME J. Vib. Acous. Vol. 124, pp. 165-171. 2002. [19] N. Haloui, D. Chikouche, M. Benidir, and R. E. Bekka , "Diagnosis of gear systems by specral analysis of vibration signals using synchronous cepstre technique," ESTS Internationl Transactions on Communication and Signal Processing, vol. 8 (1), pp. 27 -36. 2006. [20] H. Akaike, "A new look at the statistical model identification," IEEE. Transactions on automatic control, vol. AC-19 (6). 1974. [21] S. M. Kay, Modern spectral estimation, Printice hall signal processing series, Englewood cliffs: New Jersey, 1988. [22] J. A. Cadzow, "Spectral estimation: an overdetermined rational model equation approach," Proc. IEEE, vol.70 (9), pp. 907-937. 1982. [23] R. H. Jones, "Identification and autoregressive spectrum estimation," IEEE. Transaction on utomatic contr├┤l, vol. AC 131(13), 1974. [24] R. E. Bekka, and D. Chikouche, "Pouvoir de detection et de résolution de la méthode AR: Application aux signaux courts," Revue Sciences &c Technologie, Univ. Constantine, vol. 12, pp. 49- 53. 1999. [25] S. Kay, and S. L. Marpele, "Spectrum Analysis: A modern perspective," Proc. IEEE, vol. 69 (11), pp.1380-1419. 1981. [26] B. Samanta, "Gear fault detection using artificial neural networks and support vector machines with genetic algorithms," Mechanical Systems and Signal Processing, vol. 18, pp. 625-644. 2004. [27] K. R. Al- Balushi, and B. Samanta, "Gear fault diagnosis using energy- based features of acoustic emission signals," Proceedings of institution of Mechanical Engineers, Part I: Journal of Systems and control Engineering, vol. 216, pp. 249- 263. 2002. [28] L. B. Jack, A. K. Nandi, "Fault detection using support vector machines and artificial neural network augmented by genetic algorithms," Mechanical Systems and Signal Processing, vol. 16, pp. 373- 390. 2002 [29] R. B. Gibson, Power Spectral Density: a Fast, Simple Method with Low Core Storage Requirement, M.I.T. Charles Stark Draper Laboratory Press, 1972. 57 pages. [30] M. P. Norton, and. D. G. Karczub, Fundamentals of Noise and Vibration Analysis for Engineers, Cambridge University Press. 2003. [31] W. B. Davenport, and W. L. Root, An Introduction to the Theory of Random Signals and Noise, IEEE Press. (1987). [32] P. M. Frank, "Analytical and qualitative model-based fault diagnosisÔÇöa survey and some new results," European Journal of Control, vol. 2, pp. 6-28. 1996. [33] E. P. Carden, and P. Fanning, "Vibration based condition monitoring: a review," Structural Health Monitoring, vol. 3, pp. 355-377. 2004. [34] R. Isermann, "On fuzzy logic applications for automatic control, supervision, and fault diagnosis," IEEE Trans. Syst., vol. 28, pp. 221-235. 1998. [35] N. Kiupel, and P. M. Frank, "Process supervision with the aid of fuzzy logic," IEEE/SMC Conference, 1993, Vol. 2, pp. 409- 414. [36] D. Sauter, G. Dubois, E. Levrat, and J. Bremont, "Fault diagnosis in systems using fuzzy logic," Proceedings of the First European Congress on Fuzzy and Intelligent Technologies, 1993, vol. 2, pp. 781-788. [37] H. Schneider, "Implementation of a fuzzy concept for supervision and fault detection of robots," Proceedings of the First European Congress on Fuzzy and Intelligent Technologies, 1993, vol. 2, pp. 775-780. [38] R. Kumar, V. K. Jayaraman, and R. D. Kulkarni, "An SVM classifier incorporating simultaneous noise reduction and feature selection: Illustrative case examples," Pattern Recognition, vol. 38, pp. 41-49. 2005. [39] E. Brigham, Fast Fourier Transform and Its Applications, Prentice Hall Press, 1988, 416 pages. [40] T. Irvine, An Introduction to Spectral Functions, Vibration Data Press. 1998. [41] R.M. Howard, Principles of Random Signal Analysis and Low Noise Design: The Power Spectral Density and its Applications, Wiley- IEEE Press, 2002, 328 pages. [42] T. Irvine, Power Spectral Density Units: [G2 / Hz], Vibration Data Press. 2000. [43] V. T. Tran, B. S. Yang, M S. Oh, and A. C. C. Tan, "Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference," Expert Systems with Applications, vol. xxx, pp. xxx-xxx, doi:10.1016/j.eswa.2007.12.010. 2008. [44] L. C James, and S. M. Wu, "Online detection of localized defects in bearing by pattern recognition analysis," ASME Journal of Engineering Industry, vol. 111, pp. 331-336. 1989. [45] N. Saravanan, S. Cholairajan, and K. I. Ramachandran, "Vibrationbased fault diagnosis of spur bevel gear box using fuzzy technique," Expert Systems with Applications, vol. xxx, pp. xxx-xxx, doi:10.1016/j.eswa.2008.01.010. 2008. [46] K. Mollazade, H. Ahmadi, M. Omid, and R. Alimardani, "Vibration condition monitoring of hydraulic pumps using decision trees and fuzzy logic inference system," Journal of Vibration and Control, submitted for publication. [47] I. H. Witten, and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd edition, Morgan Kaufmann Press, 2005. 560 pages. [48] M. B. C. Elik, R. Bayir, "Fault detection in internal combustion engines using fuzzy logic," Proc. IMechE, Part D: Journal of Automobile Engineering, vol. 221, pp. 579-587. 2007. [49] B. Hahn, and I. Valentine, Essential MATLAB for Engineers and Scientists, 3rd Edition, Newnes Press, 2007, 448 pages.