Comparison and ensemble of temperature-based and vibration-based methods for machinery prognostics

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

James Kuria Kimotho Walter Sextro

Abstract

  1. This paper presents a comparison of a number of prognos- tic methods with regard to algorithm complexity and perfor- mance based on prognostic metrics. This information serves as a guide for selection and design of prognostic systems for real-time condition monitoring of technical systems. The methods are evaluated on ability to estimate the remaining useful life of rolling element bearing. Run-to failure vibration and temperature data is used in the analysis. The sampled prognostic methods include wear-temperature correlation method, health state estimation using temperature measurement, a multi-model particle filter approach with state equation parameter adaptation utilizing temperature measurements, prognostics through health state estimation and mapping extracted features to the remaining useful life through regression approach. Although the performance of the methods utilizing the vibration measurements is much better than the methods using temperature measurements, the methods using temperature measurements are quite promising in terms of reducing the overall cost of the condition monitoring system as well as the computational time. An ensemble of the presented methods through weighted average is also introduced. The results show that the methods are able to estimate the remaining useful life within error bounds of ±15%, which can be further reduced to ±5% with the ensemble approach.

How to Cite

Kuria Kimotho, J. ., & Sextro, W. . (2015). Comparison and ensemble of temperature-based and vibration-based methods for machinery prognostics. Annual Conference of the PHM Society, 7(1). https://doi.org/10.36001/phmconf.2015.v7i1.2596
Abstract 5 | PDF Downloads 4

##plugins.themes.bootstrap3.article.details##

Keywords

ensemble methods, combined prognostics, data fusion

References
Arulampalam, M. S., Maskell, S., N., G., & T., C. (2002). A tutorial on particle ffilter for online nonlinear/non- gaussian bayesian tracking. IEEE Transactions on Signal Processing, 50(2), 174-188.

Brecher, C., Fey, M., Hassis, A., & Bonerz, S. (2014). High- speed rolling bearing test rigs with contactless signal transmission for measuring the inner ring temperature. In European telemetry and test conference, etc2014.

Gouriveau, R., Hilairet, M., Hissel, D., Jemei, S., Jouin, M., Lechartier, E., ... Zerhouni, N. (2014). IEEE PHM 2014 data challenge: Oultine, experiments, scoring of results, winners (Tech. Rep.). IEEE 2014 PHM Challenge.

Gupta, L. A., & Peroulis, D. (2013). Wireless temperature sensor for condition monitoring of bearings operating through thick metal plates. IEEE Sensors Journal, 13(6), 2292-2298.

Harris, T., & Kotzalas, M. (2006). Advanced concepts of bearing technology: Rolling bearing analysis. CRC Press.

He, D., Bechhoefer, E., Dempsey, P., & Ma, J. (2012). An integrated approach for gear health prognostics. In Proceedings of the 2012 ahs forum.

Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006). Extreme learning machine: Theory and applications. Neuro- computing, 70, 489–501.

Johnson, K. (1985). Contact mechanics. Cambridge University Press.

Joshi, A., Marble, S., & Sadeghi, F. (2001). Bearing cage temperature measurement using radio telemetry. Journal of Engineering Tribology, 471-481.

Jouin, M., Gouriveau, R., Hissel, D., Pera, M.-C., & Zer- houni, N. (2014). Prognostics of pem fuel cell in a particle filtering framework. International Journal of Hydrogen Energy, 39, 481-494.

Kimotho, J. K., & Sextro, W. (2014a). An approach for feature extraction and selection from non-trending data for machinery prognosis. In Proceedings of the second european conference of the prognostics and health management society.

Kimotho, J. K., & Sextro, W. (2014b). Optimal parameter tuning for multiclass support vector machines in machinery health state estimation. Proceedings in Applied Mathematics and Mechanics, 14, 815-816.

Kimotho, J. K., Sondermann-Woelke, C., Meyer, T., & Sex- tro, W. (2013). Machinery prognostic method based on multiclass support vector machines and hybrid differential evelution - particle swarm optimization. Chemical Engineering Transactions, 33, 619-624.

Kimotho, J. K., Sondermann-Wo ̈lke, C., Meyer, T., & Sex- tro, W. (2013). Application of event-based decision tree and ensemble of data driven method for maintenance action recommendation. International Journal of Prognostics and Health Management, 4.

Lee, S., Cui, H., Rezvanizaniani, M., & Ni, J. (2012). Battery prognostics: Soc and soh prediction. In Proceedings of the asme 2012 international manufacturing science and engineering conference.

Meyer, T., & Sextro, W. (2014). Closed-loop control system for the reliability of intelligent mechatronic systems. In Second european conference of the prognostics and health management society.
Nectoux, P., Medjaher, K., Ramasso, E., Morello, B., & Zerhouni, N. (2012). Pronostia: An experimental platform for bearing accelerated degradation tests. IEEE International Conference on Prognostics and Health Management, Denver, CO, USA.

Sondermann-Wo ̈lke, C., & Sextro, W. (2010). Integration of condition monitoring in self-optimizing function modules applied to the active railway guidance module. International Journal on Advances in Intelligent Systems, 3, 65-74.

Tobon-Mejia, D. A., Medjaher, K., & Zerhouni, N. (2011). Estimation of the remaining useful life by using wavelet packet decomposition and hmms. In Ieee aerospace conference.

Wang, J., & Gao, R. X. (2013). Multiple model particle filfil- ter for bearing life prognosis. In 2013 IEEE conference on prognostics and health management (phm).

Xing, Y., Miao, Q., Tsui, K. L., & Petch, M. (2011). Prognostics and health monitoring for lithium-ion battery. In Intelligence and security informatics (ISI), 2011 IEEE
international conference on.
Section
Technical Papers