Unsupervised Kernel Regression Modeling Approach for RUL Prediction
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Recently, Prognostics and Health Management (PHM) has gained attention from the industrial world since it aims at increasing safety and reliability while reducing the maintenance cost by providing a useful prediction about the Remaining Useful Life (RUL) of critical components/system. In this paper, an Instance-Based Learning (IBL) approach is proposed for RUL prediction. Instances correspond to trajectories representing run-to-failure data of a component. These trajectories are modeled using Unsupervised Kernel Regression (UKR). A historical database is used to learn a UKR model for each training unit. These models fuse the run-tofailure data into a single feature that evolves over time and hence allow the construction of a library of instances. When
unseen sensory data arrive, the learned UKR models are used to construct the test degradation trajectories. RUL is deduced by comparing the test degradation trajectory to the library of instance. Only the most similar train instances are kept for RUL prediction. The proposed approach was tested and compared to approaches that apply linear regression and PCA to model the library of instances. Results highlight the benefit of using UK compared to other approaches.
How to Cite
##plugins.themes.bootstrap3.article.details##
PHM
ISO. (2004). Condition monitoring and diagnostics of machines, prognostics part 1: General guidelines (Vol. ISO/IEC Directives Part 2; Tech. Rep. No. ISO13381-1). International Organization for Standardization.
Meinicke, P., Klanke, S., Memisevic, R., & Ritter, H. (2005). Principal surfaces from unsupervised kernel regression. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 27(9), 1379–1391.
Memisevic, R. (2003). Unsupervised kernel regression for nonlinear dimensionality reduction. Unpublished doctoral dissertation, University of Bielefeld.
Mosallam, A., Medjaher, K., & Zerhouni, N. (2013). Bayesian approach for remaining useful life prediction. Chemical Engineering Transactions, 33, 139–144.
Ramasso, E., Rombaut, M., & Zerhouni, N. (2013). Joint prediction of continuous and discrete states in time-series based on belief functions. Cybernetics, IEEE Transactions on, 43(1), 37–50.
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine runto-failure simulation. In Prognostics and health management, 2008. phm 2008. international conference on (pp. 1–9).
Wang, T., Yu, J., Siegel, D., & Lee, J. (2008). A similaritybased prognostics approach for remaining useful life estimation of engineered systems. In Prognostics and health management, 2008. phm 2008. international conference on (pp. 1–6).
Xue, F., Bonissone, P., Varma, A., Yan, W., Eklund, N., & Goebel, K. (2008). An instance-based method for remaining useful life estimation for aircraft engines. Journal of Failure Analysis and Prevention, 8(2), 199–206.
Zio, E., Di Maio, F., & Stasi, M. (2010). A data-driven approach for predicting failure scenarios in nuclear systems. Annals of Nuclear Energy, 37(4), 482–491.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.