## 5 Conclusions
For example, the intelligent maintenance system for this case study at 70% detection and prediction level (able to detect failures after 70% of the asset lifetime), is estimated to improve the operational availability by 0.268% (shown in Table 9) and reduce the maintenance workload devoted to corrective maintenance by 459 h (based on Table 10) which equals 11% during 20 years of operation. Furthermore, intelligent maintenance management is also estimated to reduce the scheduled maintenance workload (that leads to downtime) by 0.339% ((3262-3251)/3262 = 0.339%), however, it will increase the scheduled maintenance workload (that does not lead to downtime) by 0.333% ((8765-8736)/8736 = 0.333%). In summary, the developed simulation model has shown the ability to estimate the lifetime benefits in terms of operational availability and its reduction of corrective maintenance workload. The authors claim that the lifetime benefits of intelligent maintenance will become even greater than what is anticipated in this paper, once other lifetime benefit aspects, which are not covered by this research, are considered. This includes lifetime benefit aspects, i.e., increasing both number and levels of detection and prediction of failure modes, improving maintenance performance by reducing the level of repair, reducing scheduled maintenance workload, enhancing asset performance, lifetime extension measures for tactical and strategical decisions, and health, safety, and environmental issues, and capital allocations. Definitely, the simulation model shall be developed further to estimate all these lifetime benefits. The structure of the developed simulation model is valid as it was extracted and validated based on experts from the case study. The structure illustrated in the statechart (Figure 1) represents (1) maintenance policy type (corrective and scheduled) and decision making (trigger and condition to get notifications), and (2) failure modes. The statechart represents how the system in this specific case company generates failure or maintenance notification and how it can trigger maintenance events. It is important to highlight that this state chart is valid for other O&G companies operating in the Norwegian Continental Shelf. Regarding the failure modes, the statechart considers all standardized failure modes (based on ISO14224) matching the well-known OREDA database. Thus, the authors claim that the presented state chart is generic for O&G compressors, while the methodology is generic for any equipment of interest. The model inputs are also analyzed in a pragmatic manner, i.e., several data sources (historical data records from the case study, OREDA, and physics-based deterioration curves). The historical data related to failure and corrective maintenance events provide valid and reliable failure rates and MTTR values, as long as the incomplete data (e.g., maintenance ending date) are manipulated. Failure rates and MTTR values extracted from the OREDA data are well known and accepted in the Norwegian O&G industry as a valid and reliable source of information. The deterioration curves extracted from Calixto [25] are also valid and reliable curves. The model outputs, i.e., simulated behaviors and estimated key performance indicators have been validated by comparing them to real-world data (case study historical data). The simulated availability and corrective maintenance timelines were validated with case company experts and numbers originating from case study literature [1]. It can be concluded that the computational model is quite effective in terms of computation time. This simulation model uses hours as time-unit, which means it simulates failure rate per hour and checks all conditions (triggers) every time unit. It takes on average around 48 h (where a \"normal computer\" is used) to provide results at equipment level, i.e., compressor. However, for future simulations, the authors recommend days as time-unit, especially once the model is scaled up to system-level, i.e., compression section and plant-level.