## 3 Case Study Analysis
### Case Study 1: Gas Export System  
This case is related to a gas export compressor system that includes redundant equipment (2 out of 4) equipped with SCADA and condition monitoring systems. Data are transferred using IoT to the cloud for analysis. The RBM program in this case incorporates three maintenance concepts: (1) scheduled maintenance campaigns to address potential issues, (2) heavy redundancy to address the system's criticality and ensure desired availability, and (3) continuous monitoring with daily and monthly remote checks. The operation and maintenance department has the goal of gaining the benefits of further advanced monitoring and predictive maintenance concepts. The main benefits are early detection and a long time to act, in addition to reducing the scheduled maintenance campaign by shifting the mindset into condition-based maintenance. The main three challenges that were faced are (1) modifying the compressor system to incorporate advanced monitoring and predictive maintenance, (2) integrating the advanced monitoring system and its notifications into the RBM program, especially the planning function, and (3) enabling the \"Planning\" and \"Analysis\" functions in the RBM program to provide insights based on multi-data sources (scheduled maintenance events, monitoring triggered events, and maintenance reports). These three challenges have created several emerging practices, summarized in Table 3. The first emerging practice is related to two new worksheets that have been used to support engineering analysis to determine the technical specifications of the advanced monitoring and predictive maintenance program. The first worksheet is the failure mode and diagnostic coverage analysis to determine the coverage of the advanced monitoring system in terms of failure modes. The second worksheet is the predictive maintenance analysis to select and evaluate the most effective diagnostic and prognostic technique. The second emerging practice is the simulation model to estimate the impact of the proposed predictive maintenance concepts on availability. The third emerging practice is a guideline to manage and emerge maintenance notifications from the predictive model with other notification sources. The new guideline handles the increase in notifications due to the monitoring system and prioritizes work orders. The guideline indicates that the prioritization process has to add the severity level of the detected fault to the failure consequence. The guideline also proposes to consider scheduled maintenance campaigns as opportunistic intervals to maintain equipment with detected faults. Therefore, the interval \begin{table}
\begin{tabular}{l l} \hline \hline
**No.** & **Emerging Practice** \\ \hline
1 & New worksheet to determine the technical specifications for the advanced monitoring and predictive maintenance program. \\
2 & Simulation model to estimate the impact of the predictive maintenance concept on the availability. \\
3 & Revise the guideline to manage maintenance notifications from different sources. \\
4 & New data flow to manage data from advanced monitoring and predictive maintenance path. \\
5 & New guideline to manage the continuous improvement of the RBM program. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The summary of emerging practices in case study 1.  
between potential failure (detected fault) and functional failure (P-F interval) is estimated (the remaining useful lifetime). The fourth emerging practice is related to the new data flow that advanced monitoring and predictive maintenance has created in the entire RBM program. The new data flow introduced new data input to maintenance reporting, for example, potential failure date, functional failure date, or repair date. The new data points have also introduced the ability to calculate the actual P-F intervals in the \"analysis\" function and reinforce them in the \"planning\" function.