# Decision Framework for Predictive Maintenance Method Selection
## 6 Conclusions  
Although this is not recognised in large part of the academic literature, practitioners still experience difficulties in the selection and application of predictive maintenance approaches. This is especially true for selecting the more advanced approaches associated with condition-based maintenance policies. To aid practitioners in the selection of the appropriate predictive maintenance approach for their situation, a selection framework was proposed, which addresses the three main problems identified:  
* Difficulty in distinguishing between the available maintenance methods;
* Mismatch between the ambition level and the required data;
* Difficulty in showing the added value of a certain maintenance approach. Subsequently, five elements were developed that solve these three problems: (_i_) the various predictive maintenance approaches have been classified into five types, ranging from the traditional experience-based approach to an advanced model-based approach; (_ii_) five ambition levels were defined that specify the required performance of the maintenance approach; (_iii_) the various types of data for predictive maintenance were classified; (_iv_) the ambition levels and data types were mapped onto the maintenance methods; (_v_) a previously presented business case method for maintenance applications was discussed. The integration of these five elements provided a framework that uses the identified dependencies and ambition levels to steer the selection of the appropriate predictive maintenance approach. Finally, four case studies have been used to test and demonstrate the proposed framework. The case studies demonstrated that the proposed selection framework helps to select the correct ambition level and the maintenance approach to achieve that. Furthermore, the framework provides insight into the required data. Thus, the proposed framework can help practitioners reduce the costly trial-and-error process in applying a predictive maintenance approach, while the individual elements developed can assist researchers in the field to better position new methods. \begin{table}
\begin{tabular}{c c} \hline \hline
**Step in Framework** & **Result and Explanation** \\ \hline Starting point & **Technology Push—ambition to use available data to enable prognostics \\ \hline \multirow{6}{*}{Ambition level} & - & Detection of anomalies and prediction of future behaviour per individual train \\  & - & Take into account actual usage \\  & - & Future conditions similar to current/historical conditions \\  & \(\rightarrow\)**Ambition level 4** \\ \hline \multirow{6}{*}{Types of data available} & - & Expert knowledge \\  & - & High-quality historical data (inspection, costing and failure data) \\ \cline{1-1}  & - & Usage data of trains \\ \cline{1-1}  & - & Condition monitoring data \\ \hline \multirow{2}{*}{Match ambition level—data?} & **Yes—AL4 requires condition monitoring data (available) and high-quality historical data (available)** \\ \hline \multirow{2}{*}{Positive business case?} & **Yes—Only minor investments required to check usability of data, high potential gain (expected extension of many maintenance intervals, reduced costs)** \\ \hline \multirow{2}{*}{MM selection} & **Advice: Apply a degradation-based maintenance approach.** \\  & This will satisfy the set ambition level \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Application of the decision framework to case 4. **Author Contributions:** Conceptualisation, W.T., J.B. and T.T. ; methodology, W.T. and J.B.; data curation, W.T. ; writing--original draft preparation, W.T. ; writing--review and editing, J.B. and T.T. ; visualisation, W.T. ; supervision, J.B. and T.T. ; funding acquisition, T.T. All authors have read and agreed to the published version of the manuscript. This research is part of the Tools4 LCM project, funded by the Netherlands Ministry of Defence and the Netherlands Aerospace Centre NLR.