PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Wavelets and principal component analysis method for vibration monitoring of rotating machinery

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Fault diagnosis is playing today a crucial role in industrial systems. To improve reliability, safety and efficiency advanced monitoring methods have become increasingly important for many systems. The vibration analysis method is essential in improving condition monitoring and fault diagnosis of rotating machinery. Effective utilization of vibration signals depends upon effectiveness of applied signal processing techniques. In this paper, fault diagnosis is performed using a combination between Wavelet Transform (WT) and Principal Component Analysis (PCA). The WT is employed to decompose the vibration signal of measurements data in different frequency bands. The obtained decomposition levels are used as the input to the PCA method for fault identification using, respectively, the Q-statistic, also called Squared Prediction Error (SPE) and the Q-contribution. Clearly, useful information about the fault can be contained in some levels of wavelet decomposition. For this purpose, the Q-contribution is used as an evaluation criterion to select the optimal level, which contains the maximum information.Associated to spectral analysis and envelope analysis, it allows clear visualization of fault frequencies. The objective of this method is to obtain the information contained in the measured data. The monitoring results using real sensor measurements from a pilot scale are presented and discussed.
Rocznik
Strony
659--670
Opis fizyczny
Bibliogr. 29 poz., rys., tab.
Twórcy
autor
  • Research Center in Industrial Technologies (CRTI), Cheraga, Algiers
  • Department of Automatic, Polytechnic National School (ENP), El-Harrach, Algiers
Bibliografia
  • 1. Al-Badour F., Sunar M., Cheded L., 2011, Vibration analysis of rotating machinery using time-frequency analysis and wavelet techniques, Mechanical Systems and Signal Processing, 25, 2083-2101
  • 2. Altmann J., 1999, Application of discrete wavelet packet analysis for the detection and diagnosis of low speed rolling-element bearing faults, Ph.D. thesis, Monash University, Melbourne, Australia
  • 3. Chiang L.H., Russell E.L., Braatz R.D., 2001, Fault Detection and Diagnosis in Industrial Systems, Springer-Verlag, London
  • 4. Chinmaya K., Mohanty A.R., 2006, Monitoring gear vibrations through motor current signature analysis and wavelet transform, Mechanical Systems and Signal Processing, 20, 1, 158-187
  • 5. Chui Ch.K., 1992, An Introduction to Wavelets, Academic Press, New York
  • 6. Daubechies I., 1988, Orthonormal bases of compactly supported wavelets, Communication on Pure and Applied Mathematics, 41, 909-996
  • 7. De Moura E.P., Souto C.R., Silva A.A., Irmao M.A.S. ˜ , 2011, Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses, Mechanical Systems and Signal Processing, 25, 5, 1765-1772
  • 8. Djebala A., Ouelaa N., Hamzaoui N., 2008, Detection of rolling bearing defects using discrete wavelet analysis, Meccanica, 43, 339-348
  • 9. Gaing Z.L., 2004, Wavelet-based neural network for power disturbance recognition and classification, IEEE Transactions on Power Delivery, 19, 1560-1568
  • 10. Gavrovska A.M., Jevtic D.R., Reljin B.D., 2009, Selection of wavelet decomposition levels in ECG filtering, Proceedings of the International Conference on Telecommunication in Modern Satellite, Cable and Broadcasting Services TELSIKS, Niˇs-Serbia, DOI: 10.1109/TELSKS.2009.5339423, 221-224
  • 11. Jackson J.E., Mudholkar G.S., 1979, Control procedures for residuals associated with principal components analysis, Technometrics, 21, 341-349
  • 12. Jolliffe I.T., 2002, Principal Component Analysis, Springer-Verlag, New York
  • 13. Kano M., Hasebe S., 2001, A new multivariate statistical process monitoring method using principal component analysis, Computers and Chemical Engineering, 25, 1103-1113
  • 14. Litak G., Sawicki J.T., 2009, Intermittent behaviour of a cracked rotor in the resonance region, Chaos, Solitons and Fractals, 42, 1495-1501
  • 15. Litak G., Sen A.K., Syta A., 2009, Intermittent and chaotic vibrations in a regenerative cutting process, Chaos, Solitons and Fractals, 41, 2115-2122
  • 16. Mallat S.G., 1989, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 7, 674-693
  • 17. Nomikos P., MacGregor J.F., 1995, Multivariate SPC charts for monitoring batch processes, Technometrics, 37, 1, 41-59
  • 18. Prabhakar S., Mohanty A.R., Sekhar A.S., 2002, Application of discrete wavelet transform for detection of ball bearings race faults, Tribology International, 35, 793-800
  • 19. Purushotham V., Narayanan S., Prasad S.A.N., 2005, Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition, NDT&E International, 38, 654-664
  • 20. Seker S., Ayaz E., 2002, A study on condition monitoring for induction motors under the accelerated aging processes, IEEE Power Engineering, 22, 7, 35-37
  • 21. Sen A.K., Litak G., Finney C.E.A., Dawc C.S., Wagner R.M., 2010, Analysis of heat release dynamics in an internal combustion engine using multifractals and wavelets, Applied Energy, 87, 1736-1743
  • 22. Shao R., Hu W., Wang Y., Qi X., 2014, The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform, Measurement, 54, 118-132
  • 23. Shibata K., Takahashi A., Shirai T., 2000, Fault diagnosis of rotating machinery through visualisation of sound signal, Mechanical Systems and Signal Processing, 14, 229-241
  • 24. Tandon N., Parey A., 2006, Condition monitoring of rotary machines, [In:] Condition Monitoring and Control for Intelligent Manufacturing, Wang L., Gao R.X. (Edit.), Springer-Verlag, London, 109-136
  • 25. Wu J.D., Liu C.H., 2008, Investigation of engine fault diagnosis using discrete wavelet transform and neural network, Expert Systems with Applications, 35, 1200-1213
  • 26. Xu X., Xiao F., Wang S., 2008, Enhanced chiller sensor fault detection, diagnosis and estimation using wavelet analysis and principal component analysis methods, Applied Thermal Engineering, 28, 226-237
  • 27. Yan R., Gao R.X., Chen X., 2014, Wavelets for fault diagnosis of rotary machines: A review with applications, Signal Processing, 96, 1-15
  • 28. Yang H., 2004, Automatic fault diagnosis of rolling element bearings using wavelet based pursuit features, Ph.D. thesis, Queensland University of Technology, Australia
  • 29. Yaqub M.F., Gondal I., Kamruzzaman J., 2011, Resonant frequency band estimation using adaptive wavelet decomposition level selection, Proceedings of the IEEE International Conference on Mechatronics and Automation ICMA, Beijing-China, DOI: 10.1109/ICMA.2011.5985687, 376-381
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniajacą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1a6224ed-ab57-4ba7-9dc6-ba2ee5042f34
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.