## 4 Results and Discussion
### Impact on the Engineering Analysis at the Project Phase
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Method** & **Purpose** \\ \hline Maintenance Concept Analysis & Explore and evaluate maintenance concepts based on the key acceptance criteria \\ \hline Failure Mode and Diagnostic Coverage Analysis & Explore relations between failure symptoms and potential diagnostic techniques \\  & Determine diagnostic coverage, i.e., how many failure modes can be detected (covered) \\ \hline Predictive Maintenance Analysis & Explore diagnostic techniques and their accuracy and detection earliness \\  & Explore predictive techniques and their accuracy and predictive horizon \\  & Determine predictive index, i.e.,how many failure modes can be diagnosed and predicted \\ \hline AR-VR-assisted Maintenance Analysis & Define the virtual and augmented elements and features \\ \hline Digital Twin Building Analysis & Define the capability level of the digital twin model and its technical specifications \\ \hline Additive Manufactured Spare Parts Analysis & Explore additive manufactured spare parts and their technical performance, e.g., strength and operational performance, e.g., lead time \\ \hline Model-based Maintenance Engineering Analysis & Define the data flow between business, engineering design and maintenance engineering documents \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Engineering analyses for risk-based maintenance in Industry 4.0 era. These emerging engineering analyses and practices, grouped and illustrated in Figure 11, are transforming risk-based maintenance to be more grounded in the asset management mindset [58]. They make the \"value\" a key driver in adapting, engineering, and managing any maintenance technology or concept. They enabled more data and rational evidence for decision making. They focus more on the condition, not only the history, and on specific context, not generic. They improved consequence awareness and risk communication through better estimates and visualizations. These emerging practices are embedding several thinking mindsets: design, life cycle, strategic, systemic, and predictive. They emphasize the proactive mindset of \"Design for\" rather than \"Manage later\". They clearly consider the life cycle phases and the associated stakeholders and needs. They encourage maintenance engineers to collaborate and gain information from stakeholders throughout the life cycle to fill these analysis worksheets. For example, the \"Failure Mode and Diagnostic Coverage Analysis\" requires information from monitoring system providers to determine the capability of their sensors to capture relevant failure symptoms, while existing maintenance systems can provide information about the relevant failure modes and symptoms. These emerging practices aim to enable strategic operating policies rather than ad hoc corrective policies and \"fix-that-fails\" situations. They are based on system thinking and capable of handling complex systems and capturing the systemic features. Although they rely on current conditions of the systems, they also predict beyond the current and explore \"what if\" scenarios. Finally, they embed agile and reinforcement learning to improve all estimations, predictions, and knowledge creation. Figure 11: Emerging mindset for risk based maintenance.