Abstract
Prognostics is the determination of condition and remaining useful life (RUL) at any time in a machine’s operations. Prognostics are based in probability, and best practice would be to provide the forecast of UL with indications of certainty where the population of similar prognostic events allows. It is also best practice to describe the impact and effects of loss of function, along with the estimated times for recovery, along with the resources which are required. Prognostics are often misunderstood with several approaches being possible to calculate RUL for machinery. This chapter describes and extends a known model (Hines 2008) for prognostics, covering all of the approaches in the model and describes how prognostics should be measured (Saxena et al. in Int J Prognostics Health Manage 2153–2648, 2008, Int J Prognostics Health Management 1:20, 2010). A brief treatment for the difficulties of validation is illustrated due to lack of failure data, along with an outline of how these deficiencies might be addressed with the advent of the information revolution and big data.
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References
Coble JB, Hines JW (2008) Prognostic algorithm categorization with PHM challenge application 1st international conference on prognostics and health management (PHM08), Denver, CO
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Saxena A, Balaban J, Saha B, Saha S, Goebel K (2008) Metrics for evaluating performance of prognostic techniques. 1st international conference on prognostics and health management (PHM 08), Denver CO
Saxena A, Celaya J, Saha B, Saha S, Goebel K (2010) Metrics for offline evaluation of prognostic performance. Int J Prognostics Health Manage, 1(1), p 20, ISSN 2153–2648
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© 2015 Springer International Publishing Switzerland
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Dibsdale, C. (2015). Holistic Prognostics. In: Redding, L., Roy, R. (eds) Through-life Engineering Services. Decision Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-12111-6_5
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DOI: https://doi.org/10.1007/978-3-319-12111-6_5
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