## 2 Materials and Methods
### Step 4: Scenario Modeling
#### 2.3.2. Intelligent Maintenance Based on Deterioration Modeling (Scenario 5)
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Component** & **Loading Equation** & **Designed Lifetime**[25] \\ \hline
**Rotor** & 0.00013738 \(\times\) AccumulatedLoading (initial AccumulatedLoading value = 0.5) & 4.4 years (38,544 h) \\ \hline
**Bearing** & 0.00010045 \(\times\) AccumulatedLoading (initial AccumulatedLoading value = 0.5) & 6.0 years (52,560 h) \\ \hline
**Seal** & 0.00075232 \(\times\) AccumulatedLoading (initial AccumulatedLoading value = 2 \(\times\) 10\({}^{-14}\)) & 4.7 years (41,172 h) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Connection between components, loading equations, and designed lifetimes. rioration) [29]. However, concerning component deterioration, the distribution of either exponential distribution or power-law distribution is most frequently adopted. Second, a designed load case that assumes constant loading from the beginning of the operation until it fails must be addressed. Such time to failure can for instance be based on recommendations from the component vendor, estimated through equations offered by the manufacturer (e.g., [30]) or other reliable data sources (e.g., Calixto [25] or OREDA [17]). In final, the suitable deterioration curve identified must be fitted with the time to failure through an iterative simulation process that highlights the entire deterioration process from a healthy component to a faulty one. In practice, the condition monitoring system monitors the deterioration process of the component during normal operation. If the monitoring system is not capable of detecting and predicting the deterioration, the accumulated deterioration level reaches the designed lifetime (100% deterioration) that triggers the \"Failure\" flow in system dynamics (Figure 5), which furthermore triggers the associated \"failure\" state in the agent-based computational model (shown in Figure 1). However, if the condition monitoring system is able to detect and predict the level of deterioration propagation prior to component failure, it tries to leverage the PdM event into a coming opportunistic maintenance interval represented by the \"OpportunisticMaintenanceInterval\" parameter connected to the \"IntelligentMaintenance\" flow. In this case, the opportunity of leveraging a predicted failure event to a future opportunistic maintenance interval is based on two criteria: (1) when the future opportunistic maintenance intervals appear and (2) the capabilities offered by the specific monitoring system, i.e., levels of detection and prediction that are demonstrated in detail in [16]. Illustratively, if the condition monitoring system is able to detect component deterioration one week prior to failure and the next opportunistic maintenance interval appears first after four weeks, there exists no opportunity to leverage the PdM into an opportunistic maintenance interval, and corrective maintenance is thus required. In contrast, if the deterioration is detected five weeks prior to component failure and the next opportunistic maintenance interval appears after four weeks, the PdM can be leveraged into the future opportunistic maintenance interval in terms of \"intelligent maintenance\". Therefore, exploiting these opportunistic maintenance intervals to perform intelligent maintenance will thus reduce the unplanned operational unavailability and cost (since corrective maintenance is replaced by intelligent maintenance). At last, this paper presents an illustrative example of how an intelligent maintenance system that enables detecting, diagnosing, and predicting the specific failure mode of breakdown (BRD) of the rotor, bearing, and seal. It is important to emphasize that the transparency between failure modes, and detection, diagnosis, and prognosis processes shall be analyzed individually for the specific condition monitoring system applied. In this context, the authors recommend the future readers perform the analysis presented in [16] to determine these specific capabilities of an associated condition monitoring system of interest.