Advertisement

Prognostics of multiple failure modes in rotating machinery using a pattern-based classifier and cumulative incidence functions

  • Ahmed Ragab
  • Soumaya Yacout
  • Mohamed-Salah Ouali
  • Hany Osman
Article

Abstract

This paper presents a novel methodology for multiple failure modes prognostics in rotating machinery. The methodology merges a machine learning and pattern recognition approach, called logical analysis of data (LAD), with non-parametric cumulative incidence functions (CIFs). It considers the condition monitoring data collected from a system that experiences several competing failure modes over its life span. LAD is used as a non-statistical classification technique to detect the actual state of the system, based on the condition monitoring data. The CIF provides an estimate for the marginal probability of each failure mode in the presence of the other competing failure modes. Accordingly, the assumption of independence between the failure modes, which is essential in many prognostic methods, is irrelevant in this paper. The proposed methodology is validated using vibration data collected from bearing test rigs. The obtained results are compared to those of two common machine learning prediction techniques: the artificial neural network and support vector regression. The comparison shows that the proposed methodology has a stable performance and can predict the remaining useful life of an individual system accurately, in the presence of multiple failure modes.

Keywords

Failure prognostics Multiple failure modes Logical analysis of data Machine learning CBM Survival analysis Rotating machinery 

Notes

Acknowledgments

This work is funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) under research Grants Numbers 141111 and 231695.

References

  1. Albert, I., Donnet, S., Guihenneuc-Jouyaux, C., Low-Choy, S., Mengersen, K., & Rousseau, J. (2012). Combining expert opinions in prior elicitation. Bayesian Analysis, 7, 503–532.CrossRefGoogle Scholar
  2. Avila-Herrera, J. F., & Subasi, M. M. (2015). Logical analysis of multi-class data. In Computing conference (CLEI), 2015 Latin American (pp. 1–10). IEEE.Google Scholar
  3. Benkedjouh, T., Medjaher, K., Zerhouni, N., & Rechak, S. (2013). Remaining useful life estimation based on nonlinear feature reduction and support vector regression. Engineering Applications of Artificial Intelligence, 26, 1751–1760.CrossRefGoogle Scholar
  4. Beyersmann, J., Allignol, A., & Schumacher, M. (2011). Competing risks and multistate models with R. New York: Springer Science & Business Media.Google Scholar
  5. Bishop, C. M. (2006). Pattern recognition and machine learning (Vol. 1). New York: Springer.Google Scholar
  6. Bocchetti, D., Giorgio, M., Guida, M., & Pulcini, G. (2009). A competing risk model for the reliability of cylinder liners in marine Diesel engines. Reliability Engineering & System Safety, 94, 1299–1307.CrossRefGoogle Scholar
  7. Boros, E., Hammer, P. L., Ibaraki, T., Kogan, A., Mayoraz, E., & Muchnik, I. (2000a). An implementation of logical analysis of data. Knowledge and Data Engineering, IEEE Transactions on, 12, 292–306.CrossRefGoogle Scholar
  8. Boros, E., Hammer, P. L., Ibaraki, T., Kogan, A., Mayoraz, E., & Muchnik, I. (2000b). An implementation of logical analysis of data. Knowledge and Data Engineering, IEEE Transactions on, 12, 292–306.CrossRefGoogle Scholar
  9. Bouckaert, R. R., Frank, E., Hall, M. A., Holmes, G., Pfahringer, B., Reutemann, P., et al. (2010). WEKA—Experiences with a Java open-source project. The Journal of Machine Learning Research, 11, 2533–2541.Google Scholar
  10. Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40, 16–28.CrossRefGoogle Scholar
  11. Chikalov, I., Lozin, V., Lozina, I., Moshkov, M., Nguyen, H. S., Skowron, A., et al. (2012). Three approaches to data analysis: Test theory, rough sets and logical analysis of data (Vol. 41). New York: Springer Science & Business Media.Google Scholar
  12. Couallier, V. (2008). A competing risks model for degradation and traumatic failure times. In Statistical models and methods for biomedical and technical systems (pp. 83–93). Boston: Birkhäuser.Google Scholar
  13. Crama, Y., Hammer, P. L., & Ibaraki, T. (1988). Cause-effect relationships and partially defined Boolean functions. Annals of Operations Research, 16, 299–325.CrossRefGoogle Scholar
  14. Dong, H., Jin, X., Lou, Y., & Wang, C. (2014). Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter. Journal of Power Sources, 271, 114–123.CrossRefGoogle Scholar
  15. Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification (2nd ed.). John Wiley.Google Scholar
  16. Dupuis, C., Gamache, M., & Pagé, J.-F. (2012). Logical analysis of data for estimating passenger show rates at Air Canada. Journal of Air Transport Management, 18, 78–81.CrossRefGoogle Scholar
  17. Elsayed, E. A. (2003). Mean residual life and optimal operating conditions for industrial furnace tubes. In W. R. Blischke & D. N. P. Murthy (Eds.), Case studies in reliability and maintenance. Hoboken, NJ: John Wiley & Sons, Inc. doi: 10.1002/0471393002.ch22.
  18. Elsayed, E. A. (2012). Reliability engineering. Hoboken: Wiley.Google Scholar
  19. Gao, R. X., & Yan, R. (2010). Wavelets: Theory and Applications for manufacturing. New York: Springer.Google Scholar
  20. Goswami, J. C., & Chan, A. K. (2011). Fundamentals of wavelets: Theory, algorithms, and applications (Vol. 233). Hoboken: Wiley.CrossRefGoogle Scholar
  21. Hammer, P. L., Kogan, A., & Lejeune, M. A. (2012). A logical analysis of banks’ financial strength ratings. Expert Systems with Applications, 39, 7808–7821.CrossRefGoogle Scholar
  22. Heng, A., Tan, A. C., Mathew, J., Montgomery, N., Banjevic, D., & Jardine, A. K. (2009a). Intelligent condition-based prediction of machinery reliability. Mechanical Systems and Signal Processing, 23, 1600–1614.CrossRefGoogle Scholar
  23. Heng, A., Tan, A. C. C., Mathew, J., Montgomery, N., Banjevic, D., & Jardine, A. K. S. (2009b). Intelligent condition-based prediction of machinery reliability. Mechanical Systems and Signal Processing, 23, 1600–1614.CrossRefGoogle Scholar
  24. Heng, A., Zhang, S., Tan, A. C., & Mathew, J. (2009c). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23, 724–739.CrossRefGoogle Scholar
  25. Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20, 1483–1510.CrossRefGoogle Scholar
  26. Kalbfleisch, J. D., & Prentice, R. L. (2011). The statistical analysis of failure time data (Vol. 360). John Wiley & Sons.Google Scholar
  27. Kim, H.-E., Tan, A. C., Mathew, J., & Choi, B.-K. (2012). Bearing fault prognosis based on health state probability estimation. Expert Systems with Applications, 39, 5200–5213.CrossRefGoogle Scholar
  28. Kleinbaum, D., & Klein, M. (2011). Survival analysis: A self-learning text, 2005. New York: Springer-Verlag.Google Scholar
  29. Klein, J., & Moeschberger, M. (1997). Survival analysis: Techniques for censored and truncated data. New York: Springer.CrossRefGoogle Scholar
  30. Kothamasu, R., Huang, S. H., & VerDuin, W. H. (2009). System health monitoring and prognostics—A review of current paradigms and practices. In Handbook of maintenance management and engineering (pp. 337–362). London: Springer.Google Scholar
  31. Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical Systems and Signal Processing, 42, 314–334.CrossRefGoogle Scholar
  32. Lehmann, A. (2009). Joint modeling of degradation and failure time data. Journal of Statistical Planning and Inference, 139, 1693–1706.CrossRefGoogle Scholar
  33. Lei, Y., He, Z., Zi, Y., & Chen, X. (2008). New clustering algorithm-based fault diagnosis using compensation distance evaluation technique. Mechanical Systems and Signal Processing, 22, 419–435.CrossRefGoogle Scholar
  34. Liu, X., Li, J., Al-Khalifa, K. N., Hamouda, A. S., Coit, D. W., & Elsayed, E. A. (2013). Condition-based maintenance for continuously monitored degrading systems with multiple failure modes. IIE Transactions, 45, 422–435.CrossRefGoogle Scholar
  35. Martin, T. G., Burgman, M. A., Fidler, F., Kuhnert, P. M., LOW-CHOY, S., McBride, M., et al. (2012). Eliciting expert knowledge in conservation science. Conservation Biology, 26, 29–38.CrossRefGoogle Scholar
  36. Misiti, M., Misiti, Y., Oppenheim, G., & Poggi, J. (2008). Matlab user’s guide: Wavelet toolbox ™ 4. Natick, MA: The Math Works Inc.Google Scholar
  37. Modi, S., Lin, Y., Cheng, L., Yang, G., Liu, L., & Zhang, W. (2011). A socially inspired framework for human state inference using expert opinion integration. Mechatronics, IEEE/ASME Transactions on, 16, 874–878.CrossRefGoogle Scholar
  38. Mortada, M.-A., Yacout, S., & Lakis, A. (2011). Diagnosis of rotor bearings using logical analysis of data. Journal of Quality in Maintenance Engineering, 17, 371–397.CrossRefGoogle Scholar
  39. Mortada, M.-A., Yacout, S., & Lakis, A. (2014). Fault diagnosis in power transformers using multi-class logical analysis of data. Journal of Intelligent Manufacturing, 25(6), 1429–1439.CrossRefGoogle Scholar
  40. Noorossana, R., & Sabri-Laghaie, K. (2015). System reliability with multiple failure modes and time scales. Quality and Reliability Engineering International, 32(3), 1109–1126. doi: 10.1002/qre.1819.CrossRefGoogle Scholar
  41. Peng, Y., Dong, M., & Zuo, M. J. (2010). Current status of machine prognostics in condition-based maintenance: A review. The International Journal of Advanced Manufacturing Technology, 50, 297–313.CrossRefGoogle Scholar
  42. Pintilie, M. (2007). Analysing and interpreting competing risk data. Statistics in Medicine, 26, 1360–1367.CrossRefGoogle Scholar
  43. Pintilie, M. (2011). An introduction to competing risks analysis. Revista Española de Cardiología (English Edition), 64, 599–605.CrossRefGoogle Scholar
  44. Prentice, R. L., Kalbfleisch, J. D., Peterson, A. V. Jr., Flournoy, N., Farewell, V. T., & Breslow, N. (1978). The analysis of failure times in the presence of competing risks. Biometrics, 34(4), 541–554.Google Scholar
  45. Qu, J., & Zuo, M. J. (2010). Support vector machine based data processing algorithm for wear degree classification of slurry pump systems. Measurement, 43, 781–791.CrossRefGoogle Scholar
  46. Ragab, A., Ouali, M.-S., Yacout, S., & Osman, H. (2014). Remaining useful life prediction using prognostic methodology based on logical analysis of data and Kaplan–Meier estimation. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-014-0926-3.
  47. Randall, R. B. (2011). Vibration-based condition monitoring: Industrial, aerospace and automotive applications. Hoboken: Wiley.CrossRefGoogle Scholar
  48. Samanta, B., Al-Balushi, K. R., & Al-Araimi, S. A. (2006). Artificial neural networks and genetic algorithm for bearing fault detection. Soft Computing, 10, 264–271.CrossRefGoogle Scholar
  49. Sapir-Pichhadze, R., Pintilie, M., Tinckam, K., Laupacis, A., Logan, A., Beyene, J., et al. (2016). Survival analysis in the presence of competing risks: The example of wait-listed kidney transplant candidates. American Journal of Transplantation, 16(7), 19581966. doi: 10.1111/ajt.13717.CrossRefGoogle Scholar
  50. Sikorska, J., Hodkiewicz, M., & Ma, L. (2011). Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing, 25, 1803–1836.CrossRefGoogle Scholar
  51. Song, S., Coit, D. W., & Feng, Q. (2016). Reliability analysis of multiple-component series systems subject to hard and soft failures with dependent shock effects. IIE Transactions, 48(8), 720–735. doi: 10.1080/0740817X.2016.1140922.CrossRefGoogle Scholar
  52. Song, S., Coit, D. W., Feng, Q., & Peng, H. (2014). Reliability analysis for multi-component systems subject to multiple dependent competing failure processes. Reliability, IEEE Transactions on, 63, 331–345.CrossRefGoogle Scholar
  53. Thumati, B. T., Feinstein, M., & Jagannathan, S. (2014). A model-based fault detection and prognostics scheme for Takagi-Sugeno fuzzy systems. Fuzzy Systems, IEEE Transactions on, 22, 736–748.CrossRefGoogle Scholar
  54. Tian, Z. (2012). An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring. Journal of Intelligent Manufacturing, 23(2), 227–237.CrossRefGoogle Scholar
  55. Vachtsevanos, G., Lewis, F., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering systems. Wiley. http://eu.wiley.com/WileyCDA/WileyTitle/productCd-047172999X.html.
  56. Vapnik, V., Golowich, S., & Smola, A. (1997). Advances in neural information processing systems 9—Proceedings of the 1996 neural information processing systems conference (NIPS 1996), Denver, CO (pp. 281–287). Cambridge, MA: MIT Press.Google Scholar
  57. Wang, H., & Gao, J. (2014). A reliability evaluation study based on competing failures for aircraft engines. Eksploatacja i Niezawodność, 16(2), 171–178.Google Scholar
  58. Wang, C.-P., & Ghosh, M. (2003). Bayesian analysis of bivariate competing risks models with covariates. Journal of Statistical Planning and Inference, 115, 441–459.CrossRefGoogle Scholar
  59. Wang, Y., & Pham, H. (2012). Modeling the dependent competing risks with multiple degradation processes and random shock using time-varying copulas. Reliability, IEEE Transactions on, 61, 13–22.CrossRefGoogle Scholar
  60. Wang, Y., Xiang, J., Markert, R., & Liang, M. (2016). Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications. Mechanical Systems and Signal Processing, 66, 679–698.CrossRefGoogle Scholar
  61. Wang, C., Xing, L., & Levitin, G. (2013). Reliability analysis of multi-trigger binary systems subject to competing failures. Reliability Engineering & System Safety, 111, 9–17.CrossRefGoogle Scholar
  62. Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques. Burlington: Morgan Kaufmann.Google Scholar
  63. Xing, L., & Levitin, G. (2010). Combinatorial analysis of systems with competing failures subject to failure isolation and propagation effects. Reliability Engineering & System Safety, 95, 1210–1215.CrossRefGoogle Scholar
  64. Yacout, S., Salamanca, D., & Mortada, M.-A. (2011). Tool and method for fault detection of devices by condition based maintenance. Google Patents.Google Scholar
  65. Yu, M., & Wang, D. (2014). Model-based health monitoring for a vehicle steering system with multiple faults of unknown types. Industrial Electronics, IEEE Transactions on, 61, 3574–3586.Google Scholar
  66. Zhang, Q., Hua, C., & Xu, G. (2014). A mixture Weibull proportional hazard model for mechanical system failure prediction utilising lifetime and monitoring data. Mechanical Systems and Signal Processing, 43, 103–112.CrossRefGoogle Scholar
  67. Zhang, Q., Tse, P. W.-T., Wan, X., & Xu, G. (2015). Remaining useful life estimation for mechanical systems based on similarity of phase space trajectory. Expert Systems with Applications, 42, 2353–2360.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Ahmed Ragab
    • 1
  • Soumaya Yacout
    • 1
  • Mohamed-Salah Ouali
    • 1
  • Hany Osman
    • 1
  1. 1.Industrial Engineering and Applied Mathematics DepartmentÉcole Polytechnique de MontréalMontrealCanada

Personalised recommendations