1Introduction to Prognostics

1.1 What Is Prognostics?

Prognostics is predictive diagnostics and provides the state of degraded health and makes an accurate prediction of when a resulting future failure in the system is likely to occur. The purpose of prognostics is to detect degradation and create predictive information such as estimates of state of health (SoH) and remaining useful life (RUL) for systems. Doing so yields the following benefits: (i) provides advance warning of failures; (ii) minimizes unscheduled maintenance; (iii) predicts the time to perform preventive replacement; (iv) increases maintenance cycles and operational readiness; (v) reduces system sustainment costs by decreasing inspection, inventory, and downtime costs; and (vi) increases reliability by improving the design and logistic support of existing systems (Pecht 2008; Kumar and Pecht 2010; O'Connor and Kleyner 2012).

Prognostics, as defined and used in this book, includes data acquisition (DA) and data manipulation (DM) by sensors (S) and processing within a sensor framework; DA, DM, and state detection (SD) employing processing and computational routines within a feature‐vector framework to produce feature data (FD) consisting of condition indicators that are leading indicators of failure (signatures); and health assessment (HA) and prognostic assessment (PA) within a prediction framework/prognostic‐information framework. The sensor framework, feature‐vector framework, prediction framework, and control ...

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