## 2 Materials and Methods
### Step 4: Scenario Modeling
#### 2.3.2. Intelligent Maintenance Based on Deterioration Modeling (Scenario 5)  
One of the main issues of applying failure rates in connection with detection, diagnosis, and prognosis purposes is due to the straight lines in terms of pulses produced by the simulation. In more detail, such straight lines make it difficult, or even impossible, to justify the opportunity to detect, diagnose, and predict future deterioration evolution. The maintenance timeline concept based on failure events is not effective to enable CBM and PdM, as they require deterioration curves instead. Therefore, a deterioration model based on loading that addresses the deterioration curves for the individual component associated with the case study must be modeled. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{6}{c}{**Input Data**} \\ \cline{2-6}  & \multicolumn{2}{c}{**Failure Rate**} & \multicolumn{2}{c}{**Deterioration Rate**} & \multicolumn{2}{c}{**MTTR**} \\ \cline{2-6}  & **Case Study** & **OREDA** & **Calixto** & **Case Study** & **OREDA** \\ \hline
1 & X & - & - & X & - \\ \hline
2 & X & & - & X\({}^{1}\) & - \\ \hline
3 & - & X & - & - & X \\ \hline
4 & X & - & - & - & X \\ \hline
5 & X & - & X & - & X \\ \hline \hline \end{tabular} \({}^{\dagger}\)Manipulated input data.  
\end{table} 
Table 6: The five use case scenarios and associated input data. The deterioration modeling process starts by first modeling the component deterioration using system dynamics, depicted in Figure 5. As seen from the loading model, it contains three different flows: (1) Loading, (2) Failure, and (3) Intelligent maintenance. Furthermore, one stock representing the \"accumulated loading\", and one parameter of \"Opportunities\" which represents the future opportunistic maintenance intervals defined by the scheduled maintenance requiring stops in operation. The logic of each flow in the deterioration and intelligent maintenance module is described in more detail in Table 7.  
In more detail, the \"Loading\" flow expresses the entire deterioration process and includes the loading equation of the specific component under study. Such an equation can be established by first identifying a failure distribution that demonstrates the evolution of a specific failure through a deterioration curve. To do so, there exist several failure distributions applied to demonstrate the degradation evolution from a healthy component to a faulty one [26; 27; 28]. Some of the most applied failure distributions concerning aging equipment are, e.g., \"traditional view\", \"bathtub curve\", and \"slow aging\" (linear det \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Element** & **Function** & **Logic** \\ \hline Loading & Estimates the loading rate per hour. & Described in Table 8. \\ \hline Accumulated Loading & Accumulates the loading rate. & Integral value of loading. \\ \hline Failure & Triggers failure and the need for corrective maintenance when the deterioration has reached a defined level. & rint(AccumulatedLoading) \(\geq\) uniform_discr (100,100)? AccumulatedLoading = initial AccumulatedLoading value: 0 \\ \hline Opportunistic Maintenance Interval & Represents the opportunistic maintenance intervals defined by the scheduled maintenance. & Defines all scheduled maintenance events that are causing a stop in the operation in terms of pulseTrains. \\ \hline Intelligent Maintenance & Triggers intelligent maintenance event when two conditions are satisfied: (1) opportunistic maintenance interval is available, and (2) the accumulated loading triggers detection or prediction alarm at the defined level. & OpportunisticMaintenancInterval! = 0 \& rint(AccumulatedLoading) \(\geq\) uniform_discr (70,100)? AccumulatedLoading = initial AccumulatedLoading value: 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Description of the deterioration and intelligent maintenance module. Figure 5: Loading model using systems dynamics.