A Survey of Data-Driven Prognostics
نویسنده
چکیده
Integrated Systems Health Management includes fault detection, fault diagnosis (or fault isolation), and fault prognosis. We define prognosis to be detecting the precursors of a failure, and predicting how much time remains before a likely failure. Algorithms that use the data-driven approach to prognosis learn models directly from the data, rather than using a hand-built model based on human expertise. This paper surveys past work in the datadriven approach to prognosis. It also includes related work in data-driven fault detection and diagnosis, and in model-based diagnosis and prognosis, particularly as applied to space systems.
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