Options for Prognostics Methods: A review of data-driven and physics- based prognostics
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Published
Oct 14, 2013
Dawn An
Nam Ho Kim
Joo-Ho Choi
Abstract
Condition-based maintenance is a cost effective maintenance strategy, in which maintenance schedules are predicted based on the results provided from diagnostics and prognostics. Although there are several reviews on diagnostics methods and CBM, a relatively small number of reviews on prognostics are available. Moreover, most of them either provide a simple comparison of different prognostics methods or focus on algorithms rather than interpreting the algorithms in the context of prognostics. The goal of this paper is to provide a practical review of prognostics methods so that beginners in prognostics can select appropriate methods for their field of applications in terms of implementation and prognostics performance. To achieve this goal, this paper introduces not only various prognostics algorithms, but also their attributes and pros and cons using simple examples.
How to Cite
An, D. ., Ho Kim, . N. ., & Choi, J.-H. . (2013). Options for Prognostics Methods: A review of data-driven and physics- based prognostics. Annual Conference of the PHM Society, 5(1). https://doi.org/10.36001/phmconf.2013.v5i1.2184
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Keywords
neural network, remaining useful life (RUL), Crack Growth, Bayesian inference, Data-driven prognostics, prognostics and health management (PHM), particle filter, physics-based prognostics, Gaussian process regression
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