###### Abstract
Therefore, the system dynamics approach came as the second wave with its ability to model interactions (causalities) between maintenance policies (corrective and preventive) to enable the field of maintenance simulation to study the effect of several and mixed maintenance policies e.g., total productive maintenance [5], reliability centered maintenance [6], overall equipment effectiveness [7], and condition-based maintenance (CBM) [8; 9; 10; 11]. In this context, simulation models involving maintenance policies using systems dynamics are nicely summarized by [PERSON], [PERSON] et al. [12]. In fact, system dynamics models are well known for their high level and abstractive representations (they consider the entire industrial system as one single system), which made maintenance practitioners and researchers search for another approach that models the individual behaviors (where they can decompose the system, but with traceable connections). Therefore, the third wave of maintenance simulation started with agent-based modeling where multi-agent models, multi-simulation models, and individual state-transition (statechart) were enabled. The agent-based models for maintenance simulation are still few, but rapidly growing [2; 13; 14; 15]. The literature clearly introduces two research gaps. First, none of the existing simulation models have modeled the deterioration based on loading. Second, a model that includes the PdM capabilities of detection, diagnosis, and prediction processes is missing. In summary, to get the lifetime benefits of the referred simulation models and make them fit with the required above-mentioned functionalities (opportunistic maintenance intervals, PdM, and load-based deterioration), further contributions are required. In fact, the future simulation model required shall be able to consider: (1) the individual agent (physical component and failure modes), as [PERSON], [PERSON] et al. [14] have done, besides, (2) modeling the PdM module, as [PERSON], [PERSON] et al. [15] have done, (3) modeling the asset determination based on loading function as [PERSON] [2] has done, and (4) modeling opportunistic maintenance intervals and leveraging PdM into these intervals in terms of intelligent maintenance. Table 1 highlights what is covered by the three latter studies and the missing scientific contribution (research gap) required to enable simulating intelligent maintenance operations. Therefore, the purpose and scientific contribution of this work is to develop a novel multi-method simulation model that enables estimating the lifetime benefits of an industrial asset, whereas intelligent maintenance is utilized as mixed maintenance strategies and the PdM is leveraged into opportunistic intervals. Leveraging PdM requires an enhanced level of detection, diagnosis, and prognosis [16] with an integrated load-based deterioration model. To be more specific, the desired simulation model shall enable simulating the behavior of several maintenance strategies and fulfill specific industrial requirements to ensure its effectiveness, fitness to purpose, and adaptability. The desired model shall enable maintenance engineers to (1) allocate \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & **Preventive and Corrective Maintenance Maintenance Conglating Agents** & **Modeling CBM and PdM** & **Load-Based Deterioration** &
\begin{tabular}{c} **Intelligent Maintenance \\ **(Leveraging PdM into Opportunistic Intervals)** \\ \end{tabular} \\ \hline
[14] & X & - & - & - \\ \hline
[15] & X & X & - & - \\ \hline
[2] & X & X & X & - \\ \hline
**This study** & X & X & X & X \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Existing research and required scientific contributions to satisfy intelligent maintenance operations.