## 3 Case Study Analysis
### Case Study 7: Offshore Wind Farm  
This case is related to the assets of offshore wind farms, including the wind turbine, blades, tower system, and substation. Wind farms are also considered unmanned assets and are well known for their costly maintenance visits and even limited access due to harsh weather conditions. Reducing operating expenses, especially maintenance expenses, is significant in obtaining a better levelized cost of energy (LCOE). Wind energy, in general, is also well known for its low production season in the summer months, which is usually utilized for maintenance purposes.  
In this case, several emerging practices have been observed, as risk-based maintenance is not a mature program as in the oil and gas sector. Several practices have been transferred from oil and gas, such as setting a technical hierarchy based on ISO 14224 [56], creating a consequence classification and risk matrix that fits the wind energy sector, performing maintenance tasks, manning study, and spare parts evaluation. It is observed that condition-based and predictive maintenance are also emerging into the risk-based maintenance program in this sector. Therefore, a new worksheet to determine the maintenance concept and the best available technology and a worksheet to determine the diagnostic coverage of the turbine monitoring system is proposed. For substations, as the system has some degree of redundancy, e.g., cooling pumps, prescriptive maintenance is introduced. In terms of Industry 4.0 technologies, this case has reported three more technologies that are explored, including inspection robots, inspection drones, and AR-assisted inspection. Therefore, a new model is created to estimate the effectiveness of these technologies and new guidelines are created to handle inspection image data (provided by robots and drones). In addition, a new worksheet is developed to evaluate and build AR-assisted inspections, to enable technicians to perform maintenance tasks easily and correctly. Several technologies were introduced in this case, where a new worksheet to explore the technology's big picture is observed. Several emerging practices have been observed to enable this improvement direction, summarized in Table 9.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**No.** & **Emerging Practice** \\ \hline
1 & New visualizations of the master data \\
2 & New predictive model to reduce master data dimensions \\
3 & New predictive model to estimate the wear evolution over time \\
4 & New model to estimate the effectiveness of RCM program \\
5 & New guideline to handle and segregate maintenance master data for RAM analysis \\ \hline \hline \end{tabular}
\end{table}
Table 8: Summary of emerging practices in case study 6.  
The observed impacts of these emerging practices are related to the \"Goals and requirements\" and \"Maintenance Program\" functions in the maintenance management loop. They enforce condition-based maintenance, robot-executed inspection, and AR-assisted inspection into the RBM program. It impacts the \"planning\", \"Execution\", \"Reporting\", and \"analysis\" functions. Emerging practices have enriched the analysis function with several data analytics related to inspection image processing. These emerging practices are also expected to provide early fault detection that enables the use of opportunistic maintenance and keeps the level of damage to a minimum. Moreover, the inspection using robots and drones is expected to reduce the \"Time to inspect\", whereas AR-assisted inspection is expected to reduce the \"Time to repair\".