Prognostics and Health Management

2Approaches for Prognosis and Health Management/Monitoring (PHM)

2.1 Introduction to Approaches for Prognosis and Health Management/Monitoring (PHM)

You learned in Chapter 1 that the purpose of prognostics is to be able to accurately detect and report a future failure in systems – to predict failure progression. Prognostic approaches in prognostics and health management/monitoring (PHM) to accomplish that purpose can be grouped into broad categories: classical, usage‐based, and condition‐based (Hofmeister et al. 2017; Pecht 2008; Kumar and Pecht 2010; O'Connor and Kleyner 2012; Sheppard and Wilmering 2009). Classical prognostic approaches can be categorized as model‐based, data‐driven, or hybrid‐driven, as shown in Figure 2.1.

Figure 2.1 Block diagram showing three approaches to PHM.

Source: based on Pecht ( 2008 ).

2.1.1 Model‐Based Prognostic Approaches

Model‐based prognostic approaches include the modeling and use of expressions related to reliability, probability, and physics of failure (PoF) models. Such models are used to study and compare, for example, the relationships of materials, manufacturing, and utilization of the reliability, robustness, and strength of a product, often in structured, designed, controlled experiments and life tests. Such modeling offers potentially good accuracy, but it is difficult to apply and use in complex, fielded systems (Speaks 2005). Those ...