Skip to main content
SpringerLink
Log in

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

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Beyersmann, J., Allignol, A., & Schumacher, M. (2011). Competing risks and multistate models with R. New York: Springer Science & Business Media.

    Google Scholar 

  • Bishop, C. M. (2006). Pattern recognition and machine learning (Vol. 1). New York: Springer.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40, 16–28.

    Article  Google Scholar 

  • 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 

  • 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.

  • Crama, Y., Hammer, P. L., & Ibaraki, T. (1988). Cause-effect relationships and partially defined Boolean functions. Annals of Operations Research, 16, 299–325.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification (2nd ed.). John Wiley.

  • 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.

    Article  Google Scholar 

  • 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.

  • Elsayed, E. A. (2012). Reliability engineering. Hoboken: Wiley.

    Google Scholar 

  • Gao, R. X., & Yan, R. (2010). Wavelets: Theory and Applications for manufacturing. New York: Springer.

    Google Scholar 

  • Goswami, J. C., & Chan, A. K. (2011). Fundamentals of wavelets: Theory, algorithms, and applications (Vol. 233). Hoboken: Wiley.

    Book  Google Scholar 

  • Hammer, P. L., Kogan, A., & Lejeune, M. A. (2012). A logical analysis of banks’ financial strength ratings. Expert Systems with Applications, 39, 7808–7821.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kalbfleisch, J. D., & Prentice, R. L. (2011). The statistical analysis of failure time data (Vol. 360). John Wiley & Sons.

  • 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.

    Article  Google Scholar 

  • Kleinbaum, D., & Klein, M. (2011). Survival analysis: A self-learning text, 2005. New York: Springer-Verlag.

    Google Scholar 

  • Klein, J., & Moeschberger, M. (1997). Survival analysis: Techniques for censored and truncated data. New York: Springer.

    Book  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Lehmann, A. (2009). Joint modeling of degradation and failure time data. Journal of Statistical Planning and Inference, 139, 1693–1706.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Misiti, M., Misiti, Y., Oppenheim, G., & Poggi, J. (2008). Matlab user’s guide: Wavelet toolbox ™ 4. Natick, MA: The Math Works Inc.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Pintilie, M. (2007). Analysing and interpreting competing risk data. Statistics in Medicine, 26, 1360–1367.

    Article  Google Scholar 

  • Pintilie, M. (2011). An introduction to competing risks analysis. Revista Española de Cardiología (English Edition), 64, 599–605.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

  • Randall, R. B. (2011). Vibration-based condition monitoring: Industrial, aerospace and automotive applications. Hoboken: Wiley.

    Book  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

  • 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 

  • Wang, C.-P., & Ghosh, M. (2003). Bayesian analysis of bivariate competing risks models with covariates. Journal of Statistical Planning and Inference, 115, 441–459.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques. Burlington: Morgan Kaufmann.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Yacout, S., Salamanca, D., & Mortada, M.-A. (2011). Tool and method for fault detection of devices by condition based maintenance. Google Patents.

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

Download references

Acknowledgments

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

Author information

Authors and Affiliations

  1. Industrial Engineering and Applied Mathematics Department, École Polytechnique de Montréal, Succ. Centre-Ville, C.P. 6079, Montreal, QC, H3C 3A7, Canada

    Ahmed Ragab, Soumaya Yacout, Mohamed-Salah Ouali & Hany Osman

Authors
  1. Ahmed Ragab
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Soumaya Yacout
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohamed-Salah Ouali
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hany Osman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Soumaya Yacout.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ragab, A., Yacout, S., Ouali, MS. et al. Prognostics of multiple failure modes in rotating machinery using a pattern-based classifier and cumulative incidence functions. J Intell Manuf 30, 255–274 (2019). https://doi.org/10.1007/s10845-016-1244-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-016-1244-8

Keywords

Search

Navigation