## 4 Results and Discussion
### Impact on the Engineering Analysis at the Project Phase  
Industry 4.0 technologies have clearly impacted the asset in terms of physical attachments (sensors, connections), digital and cyber elements (data flow, algorithms, twin models), and operating policies (use case scenarios). Thus, physical assets are somehow modified and upgraded. Several emerging engineering analyses have been observed in the case studies, which aim to determine and ensure the technical capabilities of technologically upgraded assets. The summary of these emerging engineering analyzes obtained from  
Figure 10: Maintenance interval determination and its main emerging factors. Figure 9: Maintenance scheduling and its main emerging factors. these case studies is presented in Table 14. These emerging engineering analyzes aim to engineer the potential maintenance concepts and technologies in the project phase to ensure that they will fit the purpose and provide added value during the operation (lifetime) or utilization phase. Maintenance concept selection is emerging, not only through new engineering analysis and worksheets but also as a default task in the concept planning stage within the project phase. The concept planning stage is responsible for evaluating all possible maintenance concepts that satisfy the operating concept (defined in the business planning stage) and selecting the most cost-effective concept, i.e., higher performance and availability and minimal operating expenses (OPEX). For example, the unmanned operating concept (selected in the business planning stage) shall be supported by a cost-effective maintenance concept (to be selected in the concept planning stage). Several case studies have indicated that the selected maintenance concept usually ends up as a mixed concept of corrective, scheduled, and diagnostic (condition-based). The reason behind that is mainly related to the fact that any industrial assets involve different types of assets, e.g., rotating (pumps, compressors), static (separator, vessel), safety instrument (valve) and electric (junk box, transformer), which have different loading mechanisms, failure mechanisms, symptoms, and modes. Failure mode and diagnostic coverage analysis is emerging rapidly in most maintenance engineering projects, as the condition-based maintenance concept is preferred for critical or unmanned assets. Predictive Maintenance Readiness Analysis (PdMRA) is still of limited use, as only one case has considered predictive maintenance as a possible maintenance concept. In many case studies, the concept of condition-based maintenance and predictive maintenance was mixed. However, PdMA has the potential to emerge in the future, as more companies are targeting predictive maintenance (handling both health and operating data) to obtain earlier fault notification than condition-based maintenance (only health data). For PdMA to be mature, companies have to collect data and build P-F curves for their assets and estimate the probability of diagnosis and prognosis of the most common predictive algorithms, similarly to the probability of detection (PoD) for non-destructive testing techniques. PoD is a statistical method that identifies how well an inspection procedure detects vital defects. In summary, there are emerging trends in how the industry assesses and selects condition monitoring techniques and predictive algorithms that will impact all risk-based maintenance management. AR&VR-assisted Maintenance Analysis has emerged to determine the scope and technical specifications of virtual and augmented reality applications that support maintenance work. The augmentation features could be a description of physically covered items, information or documents, and alerts to confirm the correct and complete execution of the tasks and to ensure safe working conditions (e.g., wearing the safety harness).