## 3 Results
### Maintenance Event Timelines and Workloads
#### 3.2.2 Corrective Maintenance Event Timeline  
The corrective maintenance event timelines for the five modeled use case scenarios are depicted in Figures 13-17. In the figures, the \(x\)-axis represents the simulated time in hours and the \(y\)-axis defines the state of the operation that is either characterized by 0 or 1. Furthermore, 0 defines an operational state when the equipment operates as normal and 1 defines an operational state when the equipment has failed and is out of operation due to corrective maintenance. The planned maintenance timeline, presented in Figure 12, is also included in all these corrective scenarios. The corrective maintenance event timeline shows the failure and corrective maintenance events for the most important components, i.e., bearing, rotor, seal, and motor. The corrective maintenance timeline of Scenario 1, Figure 13, clearly highlights the effect of incomplete maintenance data, where some maintenance events have quite a long maintenance time interval (due to the closing date either being missing or considered as an unreasonable extreme value). This issue was enhanced in Scenario 2, shown in Figure 14, where the closing dates were manipulated. Thus, the availability has changed from 37.756% in Scenario 1 to 73.384% in Scenario 2, which means that 35.629% of the availability in Scenario 1 is just related to incomplete maintenance dates. However, the availabilities obtained for Scenarios 1 and 2 do not match the real availability figures. Figure 12: Scheduled maintenance representing opportunistic maintenance intervals during 20 years of operation. One issue that shall be highlighted is that the time unit used for maintenance time (MTTR) in the case company is days. It means the maintenance time for any maintenance event will have a minimum duration of 24 h, even though it might take 4 h in reality. This issue can clearly be illustrated when OREDA MTTR values are used, as in Scenario 4 (Figure 16), where the availability increases from 73.384% (days used in Scenario 2) to 95.750% (hours used in Scenario 4). Thus, the time unit for the MTTR values is of significant importance, which may lead to an error of 22.366% (95.750-73.384%) in availability if days unit is used instead of hours. Figure 14: Corrective maintenance event timeline of Scenario 2 during 20 years of operation. Figure 13: Corrective maintenance event timeline of Scenario 1 during 20 years of operation. The maintenance timeline for Scenario 3 (Figure 15), which is based on OREDA failure rates and MTTR values, shows different numbers (at the component level) and locations of corrective maintenance events compared to Scenario 4 (which is based on case study failure rates and OREDA MTTR values). This is caused by the fact that the scenarios include different failure rates at the component level. However, the number of corrective maintenance events in total (at equipment level) and availability are almost the same. The corrective maintenance timeline for Scenario 5 (Figure 17) provides a smaller number of corrective maintenance events compared to Scenario 4 (Figure 16), due to the lower failure rates presented by the deterioration curves. For example, bearing deterioration curves estimate the bearing to fail every six years (around three times during 20 years of operation), while the OREDA database anticipates that the bearing will fail 26 times during 20 years of operation. In summary, the maintenance timelines (presented in Figures 13-17) visualize the number of corrective maintenance events with their associated maintenance time intervals. To get more insight into the maintenance workload for each scenario, Table 10 is provided. In addition, Table 11 presents the number of maintenance events for each scenario in more detail. Figure 16: Corrective maintenance event timeline of Scenario 4 during 20 years of operation  
Figure 15: Corrective maintenance event timeline of Scenario 3 during 20 years of operation.