Prediction of Rotating Machineries Failure by Intelligent Systems

Document Type : Research Paper


Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran


Failure of machines, due to stopping the production line, results in financial losses. Preventive maintenance, significantly extends the machineries life, and reduces the costs. On the other hand, predicting the remaining useful life (URL) of the equipment and machineries, provides adequate time for maintenance engineers to repair or replace the parts before failure occurs, and avoid the overhaul costs (conditional-based maintenance). These actions are more important for rotary machines such as turbines, pumps and compressors, than the others. Hence, in this paper, we predict the URL of the Olefin unit of Pars Petrochemical Company turbine pumps based on the bearings health by artificial neural networks (ANN) and support vector machine. First, we provided the prediction model by the RMS, mean, peak and crest factor of one bearing, which was used to estimate the RUL of the four bearings using the above methods. Results showed that the accuracy of prediction by SVM method was more than single-layer ANN.


  1. Kim, H. E., Tan, A. C. C., Mathew, J., Kim, E. Y. H. and Choi, B. K. (2009). “Prognosis of bearing failure  based on health state estimation”, Presented at the Proceedings of the 4th World Congress on Engineering Asset Management, Marriott Athens Ledra Hotel, Athens.
  2. Jardine, A. K. S., Lin, D. and Banjevic, D. (2006). “A review on machinery diagnostics and prognostics implementing condition-based maintenance”, Mechanical Systems and Signal Processing, Vol. 20, No.7, PP. 1483- 1510.
  3. Zhanj, S. and Ganesan, R. (1997). “Multivariable trend analysis using neural networks for intelligent diagnostics of rotating machinery”, Journal of Engineering for gas turbines and power, Vol. 119, No. 2, PP. 378- 384.
  4. Wang, P. and Vachtsevanos, G. (2001). “Fault prognostics using dynamic wavelet neural networks”, AI EDAM, Vol. 15, No. 4, PP. 349- 365.
  5. Yam, R. C. M., Tse, P. W., Li, L. and Tu, P. V. (2001). “Intelligent predictive decision support system for condition-based maintenance”, The International Journal of Advanced Manufacturing Technology, Vol. 17, No. 5, PP. 383- 391.
  6. Dong, Y. L., GU, Y. J., Yang, K. and Zhang, W. K. (2004). “A combining condition prediction model and its application in power plant”, Proceedings of International Conference on Machine Learning and Cybernetics IEEE. Vol. 6 , PP. 3474- 3478.
  7. Sugumaran, V., Muralidharan, V. and Ramachandran, K. I. (2007). “Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing”, Mechanical Systems and Signal Processing, Vol. 21 No. 2, PP. 930- 942.
  8. Sakthivel, N. R., Sugumaran, V. and Nair, B. B. (2010). “Application of support vector machine (SVM) and proximal support vector machine (PSVM) for fault classification of monoblock centrifugal pump”, International Journal of Data Analysis Techniques and Strategies, Vol. 2, No. 1, PP. 38- 61.
  9. Kim, H. E., Tan, A. C. C., Mathew, J. and Choi, B. K. (2012). “Bearing fault prognosis based on health state probability estimation”, Expert Systems with Applications, Vol. 39, No. 5, PP. 5200- 5213.
  10. Barakat, M., Elbadaoui, M. and Guillet, F. (2013). “Hard competitive growing neural network for the diagnosis of small bearing faults”, Mechanical Systems and Signal Processing, Vol. 37, No. 1-2, PP. 276-292.
  11. Amar, M., Gondal, I. and Wilson, C. (2013). “Multi-size-window spectral augmentation: Neural network bearing fault classifier”, In Industrial Electronics and Applications (ICIEA), 8th IEEE Conference on. PP. 261- 266.
  12. Guo, L., Li, N., Jia, F. and Lei, Y. (2017). “A recurrent neural network based health indicator for remaining useful life prediction of bearings”, Journal of Neurocomputing, Vol. 240, No. 31,  PP. 98- 109.
  13. Wang, W. Q., Golnaraghi, M. F. and Ismail, F. (2004). “Prognosis of machine health condition using neuro-fuzzy systems”, Mechanical Systems and Signal Processing, Vol. 18, No. 4, PP. 813- 831.
  14. Chinnam, R. B. and Baruah, P. (2003). “Autonomous diagnostics and prognostics through competitive learning driven HMM-based clustering In Neural Networks”, Proceedings of the International Joint Conference on, Vol. 4 ,PP. 2466- 2471.
  15. Chen, C., Vachtsevanos, G. and Orchard, M. E. (2012). “Machine remaining useful life prediction: An integrated adaptive neuro-fuzzy and high-order particle filtering approach”, Mechanical Systems and Signal Processing, Vol. 28, PP. 597- 607.
  16. Sakthivel, N. R., Sugumaran, V. and Babudevasenapati, S. (2010). “Vibration based fault diagnosis of monoblock centrifugal pump using decision tree”, Expert Systems with Applications Vol. 37 , No. 6, PP. 4040- 4049.
  17. Tian, Z. (2012). “An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring”, Journal of Intelligent Manufacturing, Vol. 23, No. 2, PP. 227- 237.
  18. Chao, H., & Byeng D. Y., Taejin, K.and Pingfeng, W. (2015). “A co-training-based approachfor prediction of remaining useful life utilizing both failure and suspension data”, Journal of Mechanical Systems and Signal Processing, Vol. 62- 63, PP. 75- 90.
  19. Engin, A. (2009). “Selecting of the optimal feature subset and kernel parameters in digital modulation classification by using hybrid genetic algorithm- support vector machines”, Expert Systems with Applications, Vol. 36, No. 2 , PP. 1391- 1402.
  20. Cristianini, N. and Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods, Cambridge University Press, Cambridge.
  21. Burges, C. (1998). “Tutorial on support vector machines for patternrecognition”, Data Mining and Knowledge Discovery,  Vol. 2, No. 2, PP. 121- 167.
  22. Bellotti, T. and Crook, J. (2008). “Support vector machines for credit scoring and discovery of significant features”, Expert Systems with Applications, Vol. 36, No. 2, PP. 102- 109.
  23. Ben-Hur, A. and Weston, J. (2010). A user’s guide to support vector machines, In: Data Mining Techniques for the Life Sciences, In Carugo, O. and Eisenhaber, F. (Eds), Totowa, NJ: Humana Press, PP. 223- 239.