An Intelligent Combined Method Based on Power Spectral Density, Decision Trees and Fuzzy Logic for Hydraulic Pumps Fault Diagnosis
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.
[1] 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.
[2] 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.
[3] 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.
[4] P.A. Laggan, "Vibration monitoring," Proc. IEE Colloquium on
Understanding your Condition Monitoring, pp. 1-11. 1999.
[5] 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.
[6] 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.
[7] H. Matuyama, "Diagnosis Algorithm," Journal of JSPE, vol. 75, pp.
35-37. 1991.
[8] 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.
[9] 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.
[1] 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.
[2] 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.
[3] 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.
[4] P.A. Laggan, "Vibration monitoring," Proc. IEE Colloquium on
Understanding your Condition Monitoring, pp. 1-11. 1999.
[5] 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.
[6] 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.
[7] H. Matuyama, "Diagnosis Algorithm," Journal of JSPE, vol. 75, pp.
35-37. 1991.
[8] 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.
[9] 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.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:50839", author = "Kaveh Mollazade and Hojat Ahmadi and Mahmoud Omid and Reza Alimardani", title = "An Intelligent Combined Method Based on Power Spectral Density, Decision Trees and Fuzzy Logic for Hydraulic Pumps Fault Diagnosis", abstract = "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.", keywords = "Power Spectral Density, Machine ConditionMonitoring, Hydraulic Pump, Fuzzy Logic.", volume = "2", number = "8", pages = "969-13", }
