# Decision Framework for Predictive Maintenance Method Selection
## 3 Design of the Framework Elements
### Element 2C: Mapping Data Inputs and Ambition Levels to the Five Maintenance Approaches  
The final step in solving problem 2 is to map the various ambition levels to the required data types and link them to the predictive maintenance methods. This will reveal whether the chosen ambition level matches the available data. Selecting the appropriate predictive maintenance method is then proposed as a trade-off between the available input and the ambition level: the ambition level prescribes the methods that should be applied, while the available data limit what is applicable. Figure 4 shows the relation between the ambition levels, the available predictive maintenance methods, and the required data types. The figure shows that with some maintenance methods, several types of ambition levels can be achieved (i.e., reliability statistics for AL 1 and AL 2). At the same time, the figure shows which data are required for each method. For example, required inputs for'method V-A' are usage or load monitoring data (2 or 3) and condition- or health-monitoring data (4). Only when these high-level data sources are available an AL 4 or 5 prognosis is feasible. Note that the differences between \begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Level of Detail in Maintenance Decision Making** \\ \hline AL 1 & Insight into future behaviour of the asset or fleet, considering static conditions \\ \hline AL 2 & Insight into future behaviour of the asset or fleet, considering differences in usage \\ \hline AL 3 & Insight into future behaviour of the asset or fleet, considering differences in usage and environmental conditions \\ \hline AL 4 & Insight into real-time deterioration of the individual asset and extrapolation to the future under constant conditions \\ \hline AL 5 & Insight into real-time deterioration of the individual asset and extrapolation to the future under largely varying conditions \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Definition of different ambition levels. \begin{table}
\begin{tabular}{c c} \hline \hline
**Type of Data** & **Description** \\ \hline \multirow{4}{*}{Historical data} & These data can be gathered from technical knowledge, inspections and historical records of failures or costs. _Low-quality_ historical data only contain the main parameters (e.g., time to failure). _High-quality_ historical data also include information on historical usage, loads (including environmental stressors) or condition/health per group of systems (fraction of fleet, i.e., a specific unit or type). Historical data are collected by manual registration, e.g., logbooks or databases, instead of detailed monitoring. \\ \hline \multirow{4}{*}{Usage monitoring} & The process of acquiring operational data, e.g., operating hours, mileage, or tons produced and/or process control data (e.g., supervisory control and data acquisition: SCADA). This preferably includes environmental data, e.g., measurements of temperature and moisture. \\ \hline \multirow{2}{*}{Load monitoring} & The process of acquiring loading data, e.g., temperature, vibration, humidity, strain or electrical current [34]. \\ \hline \multirow{4}{*}{Health or condition monitoring} & The process of acquiring signs of degradation or imminent failure, e.g., vibrations, acoustics, temperatures, wear depths or data from oil analyses (all denoted _condition monitoring_) or data extracted from the measured (dynamic) system response to identify the presence and magnitude of damage in a system, denoted as _structural health monitoring_.  
\end{table} 
Table 2: Description of data types. 'V-A' and 'V-B' are the data requirements: a data model requires high-quality historical data to train the algorithm. If these data are not available, the only other option is to use a physical model. Thus, the figure shows that there is no one-to-one relationship between the ambition levels and MMs. Reasoning from either the available data perspective or from the ambition level perspective (in most cases), several MMs are feasible.