## 5 Conclusions  
The simulated results obtained from the multi-method computational model developed in this paper clearly show the ability to estimate the lifetime benefits of applying several maintenance strategies (preventive, corrective, predictive, and opportunistic) on an industrial asset. Simulating preventive, corrective, and opportunistic maintenance is already done in literature (discussed in the introduction). The novelty and scientific contribution of this computational model is mainly traced back to its ability to (1) simulate and estimate CBM and PdM behaviors and their lifetime benefits, (2) leverage PdM into opportunistic maintenance in terms of intelligent maintenance, and (3) estimate and quantify the maintenance workload and determine the specific maintenance event timeline. Simulating CBM and PdM behaviors was enabled by the deterioration timeline concept where a deterioration curve based on loading profile is simulated, and detection and prediction levels are incorporated. In fact, most of the existing simulation models utilize the failure timeline concept generating pulse train curve, which is useless in order to incorporate detection and prediction levels. It can be concluded that the load-based deterioration curve, shown in Figure 6, is an effective concept to enable the lifetime benefits estimation of CBM and PdM. Definitely, this is a challenging issue since there are some components that either have an unknown deterioration curve or random failure curve (undetectable or unpredictable). For example, only deterioration curves for the rotor, bearing, and seal were available for this case study. The developed multi-method simulation model enables leveraging PdM capabilities into potential opportunistic intervals in terms of intelligent maintenance. It enables studying if the designed PdM specifications support gaining the lifetime benefits by utilizing potential opportunistic intervals or not. It is a core aspect to consider whether the maintenance system is intelligent or not. Intelligence in this context means that the maintenance management system is able to use detection, prediction, and scheduling analytics to optimize the maintenance events and utilize opportunistic intervals. It can be concluded based on Table 11 that the corrective maintenance events were reduced by earlier detection level or farther predictive horizon, e.g., detection and prediction at 60% of a component lifetime offers increased lifetime benefits (72.727% reduction in corrective maintenance events related to bearing, seal, and rotor) compared with the corrective maintenance reduction percent (27.272%) at 90% of asset lifetime. Please note that PdM at 90% of a component lifetime is capable of detecting sudden failures before their occurrence, however, the opportunistic intervals will not be utilized due to the short time notice. It is important to enable maintenance engineers to determine the optimal technical specifications, i.e., detection and predictive capabilities, and be able to revise and optimize such technical specifications at the design phase. Moreover, this model has adopted the \"timeline\" concept to estimate and quantify the maintenance workload amount (how much) in the specific timeline (when), rather than just the accumulated workload amount for the entire lifetime. As shown in Figures 13-17, the corrective maintenance events are time-specific. The timeline concept is required and highly useful for maintenance scheduling purposes, especially, to utilize opportunistic maintenance (based on usage or season) in an intelligent manner. Regarding the quantification of lifetime benefits of intelligent maintenance, the developed simulation model mainly covers two aspects of lifetime benefits (1) operational behavior and (2) maintenance workload.