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CLASSIFICATION OF HELICAL GEAR FAULT LEVELS USING FREQUENCY COMPONENT BASED STATISTICAL ANALYSIS WITH ANN

Year 2018, Volume: 1 Issue: 2, 76 - 86, 30.12.2018

Abstract

Gearboxes
that are frequently used as power transmission elements are experienced various
faults over time. The pitting fault, which is one of these faults, is usually
caused by insufficient lubrication and overloading. Detecting pitting fault and
identifying different pitting levels are challenging subjects in gear fault
detection. This study aims to propose a method to classify the different levels
of pitting fault in helical gearbox. It is known that gear defects illustrate
themselves in vibration signal at gear mesh frequency (GMF) and its harmonics. As
the severity of faults on the tooth surface grows, the amplitude of these
frequencies usually increases in the frequency spectrum. Frequency component
based statistical analysis (FCSA) method is utilized to obtain stronger
indicators for fault classification. In this study, frequency component based
statistical analysis calculates the mean, standard deviation, RMS and Kurtosis
values of narrowband gear vibrations obtained around the GMF and its harmonics
in order to detect these increases in the frequency spectrum. Moreover, these
statistical parameters are then used as an input for training and testing of
artificial neural network (ANN) for classification of pitting faults.
Furthermore, the pitting fault is detected and different pitting levels are classified.
It has been found that the proposed approach is quite beneficial for not only
detection, but also classification of pitting fault levels in helical
gearboxes.

References

  • 1. Öztürk H, Sabuncu M and Yesilyurt I. Early detection of pitting damage in gears using mean frequency of scalogram. Journal of Vibration and Control, 2008;14:469–484.
  • 2. Hong L and Dhupia JS. A time domain approach to diagnose gearbox fault based on measured vibration signals. Journal of Sound and Vibration, 2014;333:2164–2180.
  • 3. Elasha F, Carcel CR, Mba D, Kiat G, Nze I and Yebra G. Pitting detection in worm gearboxes with vibration analysis. Engineering Failure Analysis, 2014;42:366–376.
  • 4. Ümütlü RC, Hızarcı B, Ozturk H and Kiral Z. Pitting detection in a worm gearbox using artificial neural networks. In: Kropp W, Estorff O and Schulte-Fortkamp B. Proceedings of the 45th International Congress on Noise Control Engineering: INTER-NOISE 2016; 2016 Aug 21-24; Germany, Hamburg: German Acoustical Society (DEGA); 2016. p. 6526-6534.
  • 5. Peng Z, Kessissoglou NJ and Cox M. A study of the effect of contaminant particles in lubricants using wear debris and vibration condition monitoring techniques. Wear, 2005;258:1651–1662.
  • 6. Peng Z and Kessissoglou NJ. An integrated approach to fault diagnosis of machinery using wear debris and vibration analysis. Wear, 2003;255:1221–1232.
  • 7. Bagavathiappan S, Lahiri BB, Saravanan T, Philip J and Jayakumar T. Infrared thermography for condition monitoring–a review. Infrared Physics & Technology, 2013;60:35-55.
  • 8. Toutountzakis T, Chee KT and David M. Application of acoustic emission to seeded gear fault detection. NDT & E International, 2005;38(1):27-36.
  • 9. Worden K and Dulieu-Barton JM. An overview of intelligent fault detection in systems and structures. Structural Health Monitoring, 2004;3(1):85-98.
  • 10. Benbouzid MH. A review of induction motors signature analysis as a medium for faults detection. IEEE Transactions on Industrial Electronics, 2000;47(5):984-993.
  • 11. Ebersbach S and Peng Z. Expert system development for vibration analysis in machine condition monitoring. Expert Systems with Applications, 2008;34:291–299.
  • 12. Marquez FPG, Tobias AM, Perez JMP and Papaelias M. Condition monitoring of wind turbines: Techniques and methods. Renewable Energy, 2012;46:169–178.
  • 13. Zarei J, Tajeddini MA and Karimi HR. Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics, 2014;24:151–157.
  • 14. Jardine AKS, Lin D, Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 2006;20:1483–1510.
  • 15. Kankar PK, Sharma CS and Harsha SP. Fault diagnosis of ball bearings using machine learning methods. Expert Systems with Applications, 2011;38(3):1876-1886.
  • 16. Jamil M, Sharma SK and Singh R. Fault detection and classification in electrical power transmission system using artificial neural network. SpringerPlus, 2015;4(1):334-13.
  • 17. Samantha B. Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mechanical Systems and Signal Processing, 2004;18:625–644.
  • 18. Samanta B, Al-Balushi KR and Al-Araimi SA. Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Engineering Applications of Artificial Intelligence, 2003;16(7):657-665.
  • 19. Rumelhart DE and McClelland JL. Parallel distributed processing: explorations in the microstructure of cognition. United States: MIT Press; 1986.
  • 20. Sorsa T, Koivo HN and Koivisto H. Neural networks in process fault diagnosis. IEEE Transactions on Systems, 1991;21:815-825.
  • 21. McCormick AC and Nandi AK. Classification of the rotating machine condition using artificial neural networks. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 1997;211:439–450.
  • 22. Samanta B and Al-Balushi KR. Use of time domain features for the neural network based fault diagnosis of a machine tool coolant system. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 2001;215:199–207.
  • 23. Kang Y, Wang CC, Chang YP, Hsueh CC and Chang MC. Certainty improvement in diagnosis of multiple faults by using versatile membership functions for fuzzy neural networks. In: Wang J, Yi Z, Zurada JM, Lu B-L and Yin H. Third International Symposium on Neural Networks - Advances in Neural Networks. Berlin: Springer; 2006. p. 370-375.
  • 24. Zhang X, Xiao L, and Kang J. Application of an improved Levenberg-Marquardt back propagation neural network to gear fault level identification. Journal of Vibroengineering, 2014;16(2):855-868.
  • 25. Li Z, Yan X, Yuan C, Zhao J and Peng Z. Fault detection and diagnosis of a gearbox in marine propulsion systems using bispectrum analysis and artificial neural networks. Journal of Marine Science and Application, 2011;10(1):17-24.
  • 26. Huang Q, Jiang D, Hong L and Ding Y. Application of wavelet neural networks on vibration fault diagnosis for wind turbine gearbox. In: Sun F, Zhang J, Tan Y, Cao J and Yu W. 5th International Symposium on Neural Networks - Advances in Neural Networks. Berlin: Springer; 2008. p. 313-320.
  • 27. Jia F, Lei Y, Lin J, Zhou X and Lu N. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical Systems and Signal Processing, 2016;72:303-315.
  • 28. Jia-li T, Yi-jun L and Fang-sheng W. Levenberg-Marquardt neural network for gear fault diagnosis. In: Proceeding book of 2010 International Conference on Networking and Digital Society: ICNDS 2010; 2010 May 30-31; China. Wenzhou: IEEE Xplore; 2010. p. 134-137.
  • 29. Kůrková V. Kolmogorov's theorem and multilayer neural networks. Neural Networks, 1992;5(3):501-506.
  • 30. Qu Y, He M, Deutsch J and He D. Detection of pitting in gears using a deep sparse autoencoder. Applied Sciences, 2017;7(5):515-15.
  • 31. Hajnayeb A, Ghasemloonia A, Khadem SE and Moradi MH. Application and comparison of an ANN-based feature selection method and the genetic algorithm in gearbox fault diagnosis. Expert Systems with Applications, 2011;38(8):10205-10209.
Year 2018, Volume: 1 Issue: 2, 76 - 86, 30.12.2018

Abstract

References

  • 1. Öztürk H, Sabuncu M and Yesilyurt I. Early detection of pitting damage in gears using mean frequency of scalogram. Journal of Vibration and Control, 2008;14:469–484.
  • 2. Hong L and Dhupia JS. A time domain approach to diagnose gearbox fault based on measured vibration signals. Journal of Sound and Vibration, 2014;333:2164–2180.
  • 3. Elasha F, Carcel CR, Mba D, Kiat G, Nze I and Yebra G. Pitting detection in worm gearboxes with vibration analysis. Engineering Failure Analysis, 2014;42:366–376.
  • 4. Ümütlü RC, Hızarcı B, Ozturk H and Kiral Z. Pitting detection in a worm gearbox using artificial neural networks. In: Kropp W, Estorff O and Schulte-Fortkamp B. Proceedings of the 45th International Congress on Noise Control Engineering: INTER-NOISE 2016; 2016 Aug 21-24; Germany, Hamburg: German Acoustical Society (DEGA); 2016. p. 6526-6534.
  • 5. Peng Z, Kessissoglou NJ and Cox M. A study of the effect of contaminant particles in lubricants using wear debris and vibration condition monitoring techniques. Wear, 2005;258:1651–1662.
  • 6. Peng Z and Kessissoglou NJ. An integrated approach to fault diagnosis of machinery using wear debris and vibration analysis. Wear, 2003;255:1221–1232.
  • 7. Bagavathiappan S, Lahiri BB, Saravanan T, Philip J and Jayakumar T. Infrared thermography for condition monitoring–a review. Infrared Physics & Technology, 2013;60:35-55.
  • 8. Toutountzakis T, Chee KT and David M. Application of acoustic emission to seeded gear fault detection. NDT & E International, 2005;38(1):27-36.
  • 9. Worden K and Dulieu-Barton JM. An overview of intelligent fault detection in systems and structures. Structural Health Monitoring, 2004;3(1):85-98.
  • 10. Benbouzid MH. A review of induction motors signature analysis as a medium for faults detection. IEEE Transactions on Industrial Electronics, 2000;47(5):984-993.
  • 11. Ebersbach S and Peng Z. Expert system development for vibration analysis in machine condition monitoring. Expert Systems with Applications, 2008;34:291–299.
  • 12. Marquez FPG, Tobias AM, Perez JMP and Papaelias M. Condition monitoring of wind turbines: Techniques and methods. Renewable Energy, 2012;46:169–178.
  • 13. Zarei J, Tajeddini MA and Karimi HR. Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics, 2014;24:151–157.
  • 14. Jardine AKS, Lin D, Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 2006;20:1483–1510.
  • 15. Kankar PK, Sharma CS and Harsha SP. Fault diagnosis of ball bearings using machine learning methods. Expert Systems with Applications, 2011;38(3):1876-1886.
  • 16. Jamil M, Sharma SK and Singh R. Fault detection and classification in electrical power transmission system using artificial neural network. SpringerPlus, 2015;4(1):334-13.
  • 17. Samantha B. Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mechanical Systems and Signal Processing, 2004;18:625–644.
  • 18. Samanta B, Al-Balushi KR and Al-Araimi SA. Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Engineering Applications of Artificial Intelligence, 2003;16(7):657-665.
  • 19. Rumelhart DE and McClelland JL. Parallel distributed processing: explorations in the microstructure of cognition. United States: MIT Press; 1986.
  • 20. Sorsa T, Koivo HN and Koivisto H. Neural networks in process fault diagnosis. IEEE Transactions on Systems, 1991;21:815-825.
  • 21. McCormick AC and Nandi AK. Classification of the rotating machine condition using artificial neural networks. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 1997;211:439–450.
  • 22. Samanta B and Al-Balushi KR. Use of time domain features for the neural network based fault diagnosis of a machine tool coolant system. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 2001;215:199–207.
  • 23. Kang Y, Wang CC, Chang YP, Hsueh CC and Chang MC. Certainty improvement in diagnosis of multiple faults by using versatile membership functions for fuzzy neural networks. In: Wang J, Yi Z, Zurada JM, Lu B-L and Yin H. Third International Symposium on Neural Networks - Advances in Neural Networks. Berlin: Springer; 2006. p. 370-375.
  • 24. Zhang X, Xiao L, and Kang J. Application of an improved Levenberg-Marquardt back propagation neural network to gear fault level identification. Journal of Vibroengineering, 2014;16(2):855-868.
  • 25. Li Z, Yan X, Yuan C, Zhao J and Peng Z. Fault detection and diagnosis of a gearbox in marine propulsion systems using bispectrum analysis and artificial neural networks. Journal of Marine Science and Application, 2011;10(1):17-24.
  • 26. Huang Q, Jiang D, Hong L and Ding Y. Application of wavelet neural networks on vibration fault diagnosis for wind turbine gearbox. In: Sun F, Zhang J, Tan Y, Cao J and Yu W. 5th International Symposium on Neural Networks - Advances in Neural Networks. Berlin: Springer; 2008. p. 313-320.
  • 27. Jia F, Lei Y, Lin J, Zhou X and Lu N. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical Systems and Signal Processing, 2016;72:303-315.
  • 28. Jia-li T, Yi-jun L and Fang-sheng W. Levenberg-Marquardt neural network for gear fault diagnosis. In: Proceeding book of 2010 International Conference on Networking and Digital Society: ICNDS 2010; 2010 May 30-31; China. Wenzhou: IEEE Xplore; 2010. p. 134-137.
  • 29. Kůrková V. Kolmogorov's theorem and multilayer neural networks. Neural Networks, 1992;5(3):501-506.
  • 30. Qu Y, He M, Deutsch J and He D. Detection of pitting in gears using a deep sparse autoencoder. Applied Sciences, 2017;7(5):515-15.
  • 31. Hajnayeb A, Ghasemloonia A, Khadem SE and Moradi MH. Application and comparison of an ANN-based feature selection method and the genetic algorithm in gearbox fault diagnosis. Expert Systems with Applications, 2011;38(8):10205-10209.
There are 31 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Rafet Can Ümütlü

Berkan Hızarcı

Hasan Ozturk

Zeki Kıral

Publication Date December 30, 2018
Submission Date October 22, 2018
Acceptance Date December 23, 2018
Published in Issue Year 2018 Volume: 1 Issue: 2

Cite

APA Ümütlü, R. C., Hızarcı, B., Ozturk, H., Kıral, Z. (2018). CLASSIFICATION OF HELICAL GEAR FAULT LEVELS USING FREQUENCY COMPONENT BASED STATISTICAL ANALYSIS WITH ANN. Usak University Journal of Engineering Sciences, 1(2), 76-86.
AMA Ümütlü RC, Hızarcı B, Ozturk H, Kıral Z. CLASSIFICATION OF HELICAL GEAR FAULT LEVELS USING FREQUENCY COMPONENT BASED STATISTICAL ANALYSIS WITH ANN. UUJES. December 2018;1(2):76-86.
Chicago Ümütlü, Rafet Can, Berkan Hızarcı, Hasan Ozturk, and Zeki Kıral. “CLASSIFICATION OF HELICAL GEAR FAULT LEVELS USING FREQUENCY COMPONENT BASED STATISTICAL ANALYSIS WITH ANN”. Usak University Journal of Engineering Sciences 1, no. 2 (December 2018): 76-86.
EndNote Ümütlü RC, Hızarcı B, Ozturk H, Kıral Z (December 1, 2018) CLASSIFICATION OF HELICAL GEAR FAULT LEVELS USING FREQUENCY COMPONENT BASED STATISTICAL ANALYSIS WITH ANN. Usak University Journal of Engineering Sciences 1 2 76–86.
IEEE R. C. Ümütlü, B. Hızarcı, H. Ozturk, and Z. Kıral, “CLASSIFICATION OF HELICAL GEAR FAULT LEVELS USING FREQUENCY COMPONENT BASED STATISTICAL ANALYSIS WITH ANN”, UUJES, vol. 1, no. 2, pp. 76–86, 2018.
ISNAD Ümütlü, Rafet Can et al. “CLASSIFICATION OF HELICAL GEAR FAULT LEVELS USING FREQUENCY COMPONENT BASED STATISTICAL ANALYSIS WITH ANN”. Usak University Journal of Engineering Sciences 1/2 (December 2018), 76-86.
JAMA Ümütlü RC, Hızarcı B, Ozturk H, Kıral Z. CLASSIFICATION OF HELICAL GEAR FAULT LEVELS USING FREQUENCY COMPONENT BASED STATISTICAL ANALYSIS WITH ANN. UUJES. 2018;1:76–86.
MLA Ümütlü, Rafet Can et al. “CLASSIFICATION OF HELICAL GEAR FAULT LEVELS USING FREQUENCY COMPONENT BASED STATISTICAL ANALYSIS WITH ANN”. Usak University Journal of Engineering Sciences, vol. 1, no. 2, 2018, pp. 76-86.
Vancouver Ümütlü RC, Hızarcı B, Ozturk H, Kıral Z. CLASSIFICATION OF HELICAL GEAR FAULT LEVELS USING FREQUENCY COMPONENT BASED STATISTICAL ANALYSIS WITH ANN. UUJES. 2018;1(2):76-8.

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