# Decision Framework for Predictive Maintenance Method Selection
## 3 Design of the Framework Elements
### Element 1: Classification of the Available Predictive Maintenance Approaches
**Degradation-based predictions** are based on the extrapolation of a general path of a degradation measure to a predetermined failure threshold. By measuring symptoms of incipient failure, e.g., rises in temperature or vibration, the system can be diagnosed. The prognosis is inferred from sensor readings, i.e., is always based on _measurements_, e.g., from a condition monitoring or structural health monitoring system. The prediction starts from the current state of degradation and results in an expected remaining lifetime of a specific (individual) system in a specific environment. 5. **Model-based predictions** give the expected remaining lifetime of a specific (individual) system under specified conditions. Two types of model-based approaches can be followed: 1. _Physical model-based_: The prognostic parameter is _calculated_ using a physical model of the degradation mechanism based on direct sensing of the loads or usage that govern the critical failure mechanisms of individual components. 6. _Data model-based_: The prognostic parameter is _calculated_ or _inferred_ using data analytics that uses sensed variations in loads, usage data, process data or condition/health monitoring data as input. The algorithms aim to derive patterns or relations in the data or try to detect anomalies by comparing them to historical data. These five approaches all combine specific maintenance policies with certain methods and techniques. To clarify the link between policies and the proposed methods, Figure 2 schematically shows their relations. Note that in a bottom-up reasoning approach, a certain predictive maintenance method can only be combined with one specific policy: e.g., following the model-based method (V) automatically means that a condition-based policy is adopted. However, the other way around, selecting a condition-based maintenance policy (e.g., using some policy selection method) still leaves several options for the complete maintenance approach (III, IV and V). The present paper especially focuses on this final step. An important aspect in differentiating the various approaches is how precisely the lifetime of a system can be predicted. The lower maturity methods of experience-based (I)  
Figure 2: Relation between predictive maintenance policies and proposed types of MMs (and their clustering in approaches I to V). and reliability statistics (II) cannot predict the lifetime of an individual system or take into account a specific operating condition; they only provide fleetwide and long-time averages, e.g., an MTBF value. This branch is therefore referred to as 'fleetwide' in Figure 2. It could be argued whether these methods are true predictive maintenance methods, as they do not allow for failure predictions for individual systems. The more advanced approaches, III, IV and V, are based on condition-based policies, which differentiate between individual systems or operating contexts. However, a model-based approach (V) can also predict the lifetime for not previously encountered situations, while the degradation-based approach (IV) can only rely on extrapolating the current trend.