Journal of Mechanical Science and Technology

, Volume 28, Issue 1, pp 61–71 | Cite as

A machine learning approach for the condition monitoring of rotating machinery

  • Dimitrios Kateris
  • Dimitrios Moshou
  • Xanthoula-Eirini Pantazi
  • Ioannis Gravalos
  • Nader Sawalhi
  • Spiros Loutridis
Article

Abstract

Rotating machinery breakdowns are most commonly caused by failures in bearing subsystems. Consequently, condition monitoring of such subsystems could increase reliability of machines that are carrying out field operations. Recently, research has focused on the implementation of vibration signals analysis for health status diagnosis in bearings systems considering the use of acceleration measurements. Informative features sensitive to specific bearing faults and fault locations were constructed by using advanced signal processing techniques which enable the accurate discrimination of faults based on their location. In this paper, the architecture of a diagnostic system for extended faults in bearings based on neural networks is presented. The multilayer perceptron (MLP) with Bayesian automatic relevance determination has been applied in the classification of accelerometer data. New features like the line integral and feature based sensor fusion are introduced which enhance the fault identification performance. Vibration feature selection based on Bayesian automatic relevance determination is introduced for finding better feature combinations.

Keywords

Condition monitoring Vibrations Neural networks Sensor fusion Feature selection 

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References

  1. [1]
    D. M. Himmelblau, Fault detection and diagnosis in chemical and petrochemical processes, Elsevier Press, Amsterdam (1978).Google Scholar
  2. [2]
    G. Craessaerts, J. De Baerdemaeker and W. Saeys, Fault diagnostic systems for agricultural machinery, Biosystems Engineering, 106(1) (2010) 26–36.CrossRefGoogle Scholar
  3. [3]
    V. Purushotham, S. Narayanan and S. A. N. Prasad, Multifault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition, NDT&E International (2005) 1–11.Google Scholar
  4. [4]
    D. Bently, Bently Nevada Co. Predictive maintenance through the monitoring and diagnostics of rolling element bearings, Applications Note ANO44 (1989) 2–8.Google Scholar
  5. [5]
    F. K. Choy, J. Zhou, M. J. Braun and L. Wang, Vibration monitoring and damage quantification of fault ball bearings, Transactions of the ASME, 127 (2005) 776–783.Google Scholar
  6. [6]
    B. Samanta, K. R. Al-Balushi and S. A. Al-Araimi, Artificial neural networks and genetic algorithm for bearing fault detection, Soft Computing, 10 (2006) 264–271.CrossRefGoogle Scholar
  7. [7]
    Yean-Ren Hwang, Kuo-Kuang Jen and Yu-Ta Shen, Application of cepstrum and neural network to bearing fault detection, Journal of Mechanical Science and Technology, 23 (2009) 2730–2737.CrossRefGoogle Scholar
  8. [8]
    V. Sugumaran and K. I. Ramachandran, Effect of number of features on classi?cation of roller bearing faults using SVM and PSVM, Expert Systems with Applications, 38 (2011) 4088–4096.CrossRefGoogle Scholar
  9. [9]
    A. Widodo, E. Kim, J-D. Son, B-S. Yang, A. Tan, D-S. Gu, B-K. Choi and L. Mathew, Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine, Expert Systems with Applications, 36(2) (2009) 7252–7261.CrossRefGoogle Scholar
  10. [10]
    Bo Suk Yang, Tian Han and Won-Woo Hwang, Fault diagnosis of rotating machinery based on multi-class support vector machines, Journal of Mechanical Science and Technology, 19(3) (2005) 846–859.CrossRefGoogle Scholar
  11. [11]
    C. J. C. Burgess, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, 2 (1998) 955–974.Google Scholar
  12. [12]
    S. Tyagi, A comparative study of SVM classifiers and artificial neural networks application for rolling element bearing fault diagnosis using wavelet transform preprocessing. In Proceedings of World Academy of Science, Engineering And Technology, 33 (2008) 319–327.Google Scholar
  13. [13]
    P. K. Kankar, S. C. Sharma and S. P. Harsha, Fault diagnosis of ball bearings using machine learning methods, Expert Systems with Applications, 38(3) (2011) 1876–1886.CrossRefGoogle Scholar
  14. [14]
    J. Chenga, D. Yua, J. Tangb and Y. Yanga, Application of SVM and SVD technique based on EMD to the fault diagnosis of the rotating machinery, Shock and Vibration, 16 (2009) 89–98.CrossRefGoogle Scholar
  15. [15]
    V. Sugumaran, G. R. Sabareesh and K. I. Ramachandran, Fault diagnostics of roller bearing using kernel based neighborhood score multi-class support vector machine, Expert Systems with Applications, 34(4) (2008) 3090–3098.CrossRefGoogle Scholar
  16. [16]
    Y. Yang, D. Yu and J. Cheng, A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM, Measurement, 40(9–10) (2007a) 943–950.CrossRefGoogle Scholar
  17. [17]
    J. Yang, Y. Zhang and Y. Zhu, Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension, Mechanical Systems and Signal Processing, 21(5) (2007b) 2012–2024.CrossRefGoogle Scholar
  18. [18]
    B. Li, M. Y. Chow, Y. Tipsuwan and J. C. Hung, Neuralnetwork-based motor rolling bearing fault diagnosis, IEEE Transactions on Industrial Electronics, 47 (2000) 1060–1069.CrossRefGoogle Scholar
  19. [19]
    Ngoc-Tu Nguyen, Hong-Hee Lee and Jeong-Min Kwon, Optimal feature selection using genetic algorithm for mechanical fault detection of induction motor, Journal of Mechanical Science and Technology, 22 (2008) 490–496.CrossRefGoogle Scholar
  20. [20]
    R. P. W. Duin, P. Juszczak, P. Paclik, E. Pekalska, D. de Ridder, D. M. J. Tax and S. Verzakov, PRTools 4.1, A matlab toolbox for pattern recognition, Delft University of Technology (2007).Google Scholar
  21. [21]
    Z. Xu, J. Xuan, T. Shi, B. Wu and Y. Hu, Application of a modified fuzzy ARTMAP with feature-weight learning for the fault diagnosis of bearing, Expert Systems with Applications, 36 (2009) 9961–9968.CrossRefGoogle Scholar
  22. [22]
    P. D. McFadden and J. D. Smith, Vibration monitoring of rolling element bearings by the high-frequency resonance technique — A review, Tribology International, 17(1) (1984) 3–10.CrossRefGoogle Scholar
  23. [23]
    B. A. Paya, I. I. Esat and M. N. M. Badi, Artificial neural network based fault diagnostics of rotating machinery using wavelet transforms as a pre-processor, Mechanical Systems and Signal Processing, 11(5) (1997) 751–765.CrossRefGoogle Scholar
  24. [24]
    N. Sawalhi, Diagnostics, Prognostics and fault simulation for rolling element bearings, PhD Thesis, University of New South Wales, Australia (2007).Google Scholar
  25. [25]
    R. B. Randall, Vibration-based condition monitoring: industrial, aerospace and automotive applications, Chichester, West Sussex: John Wiley & Sons Ltd. (2011).CrossRefGoogle Scholar
  26. [26]
    N. Sawalhi and R. B. Randall, Simulating gear and bearing interactions in the presence of faults Part II: Simulation of the vibrations produced by extended bearing faults, Mechanical Systems and Signal Processing, 22 (2008) 1952–1966.CrossRefGoogle Scholar
  27. [27]
    G. K. Singh and S. A. Ahmed, Vibration signal analysis using wavelet transform for isolation and identification of electrical faults in induction machine, Electric Power Systems Research, 68(2) (2003) 119–136.CrossRefGoogle Scholar
  28. [28]
    Y. Lei, Z. He and Y. Zi, Application of an intelligent classification method to mechanical fault diagnosis, Expert Systems and Applications, 36 (2009) 9941–9948.CrossRefGoogle Scholar
  29. [29]
    D. E. Rumelhart, G. E. Hinton and R. J. Williams, Learning internal representations by error propagation, In: Rumelhart D E, McClelland J L (Eds.), Parallel Distributed Processing (Part 1). MIT Press, Cambridge, USA (1986) 318–362.Google Scholar
  30. [30]
    C. M. Bishop, Neural networks for pattern recognition, 1st ed., Oxford University Press, New York, USA (1995).Google Scholar
  31. [31]
    D. J. C. MacKay, A practical Bayesian framework for back-propagation networks, Neural Computation 4(3) (1992) 448–472.CrossRefGoogle Scholar
  32. [32]
    C. Bravo; D. Moshou, J. West, A. McCartney and H. Ramon, Early disease detection in wheat fields using spectral reflectance, Biosystems Engineering, 84(2) (2003) 137–145.CrossRefGoogle Scholar
  33. [33]
    E. Pękalska and R. P. W. Duin, Classifiers for dissimilarity-based pattern recognition, oral presentation, in: A. Sanfeliu, J. J. Villanueva, M. Vanrell, R. Alquezar, A. K. Jain, J. Kittler (eds.), ICPR15, Proc. 15th Int. Conference on Pattern Recognition (Barcelona, Spain, Sep.3–7), vol. 2, Pattern Recognition and Neural Networks, IEEE Computer Society Press, Los Alamitos, 12–16 (2000).Google Scholar
  34. [34]
    I. T. Nabney, Netlab: Algorithms for pattern recognition, Springer, London, (2001).Google Scholar
  35. [35]
    D. Moshou, D. Kateris, N. Sawalhi, I. Gravalos, S. Loutridis, T. Gialamas, P. Xyradakis and Z. Tsiropoulos, Condition monitoring of mechanical subsystems of agricultural vehicles based on fusion of vibration features, In Proceedings of XXXIV CIOSTA CIGR V Conference 2011: Efficient and safe production processes in sustainable agriculture and forestry, Vienna, Austria (2011).Google Scholar

Copyright information

© The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Dimitrios Kateris
    • 1
  • Dimitrios Moshou
    • 1
  • Xanthoula-Eirini Pantazi
    • 1
  • Ioannis Gravalos
    • 2
  • Nader Sawalhi
    • 3
  • Spiros Loutridis
    • 4
  1. 1.Agricultural Engineering Laboratory, School of AgricultureAristotle UniversityThessalonikiGreece
  2. 2.Department of Biosystems Engineering, School of Agricultural TechnologyTechnological Educational Institute of LarissaLarissaGreece
  3. 3.School of Mechanical EngineeringPrince Mohammad Bin Fahd UniversityAl KhobarKingdom of Saudi Arabia
  4. 4.Department of Electrical Engineering, School of Technological ApplicationsTechnological Educational Institute of LarissaLarissaGreece

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