## 4 Results and Discussion
### Impact on the Risk Matrix  
During the extraction of emerging practices in the selected case studies, concern about how the risk matrix is affected by these emerging practices was a dominant concern. The concern can be categorized into three aspects: (1) emerging practices change the volume and variety of data that feed the risk matrix, (2) emerging practices directly affect the likelihood of failure, and (3) emerging practices directly affect the consequences of failure. These three aspects generate a more dynamic risk matrix that needs more frequent updates. Regarding data volume, in some case studies, condition-based and predictive maintenance programs have increased the number of fault notifications and censored data. This phenomenon has increased the number of potential recorded failures. This phenomenon presents some challenges and opportunities. The estimations of the mean time between failures, the mean time to failure, and the mean time to repair are more challenging as more uncertainties are introduced. A new way to handle potential failures and their accuracy and reinforcement process shall be introduced. Less corrective maintenance events are expected to appear, which means that fewer random events are introduced into the risk matrix. However, more planned preventive maintenance events are expected, which means that the time between failures does not depend totally on the reliability of the components; the P-F interval time is also implicitly captured. Table 12 illustrates the observed or expected impact of emerging practices on the risk matrix for each case study. Condition-based and predictive maintenance concepts and their associated emerging practices affecting The likelihood of registered failure as a reduction in registered functional failures is expected and an increase in the registered potential failures is expected. However, it was observed that registered functional failures are usually for multiple components as the level of damage is high. The registered potential failures are for single components as \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Aspect** & **Case 1** & **Case 2** & **Case 3** & **Case 4** & **Case 5** & **Case 6** & **Case 7** \\ \hline Installation type & Midstream & Topside & Topside & Topside & Subsea & Drilling & Wind farm \\ Equipment type & Rotating & Rotating & Static & Safety & Safety & Rotating & Rotating \\ \hline Potential failures & X & X & X & X & X & X & X \\ Functional failures & X & X & X & X & X & X & X \\ Mean time between failures & X & X & X & X & X & X & X \\ Human errors & & & & & & & \\ Safety incidents & X & X & X & X & X & X & X \\ Level of damage & X & X & X & X & X & X & X \\ Mean time to repair & & & X & & & & X \\ Production consequence & X & X & X & X & X & X & X \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The summary of emerging practices impacts on the risk matrix. the level of damage is low. In summary, the number of failures at system level is expected to be reduced. It is worth mentioning that most of these monitoring systems were recently installed and data were not collected for a long time to be conclusive in terms of impacts and benefits. Moreover, it depends on the policy of how to deal with fault notifications generated by the monitoring systems, whether actions are taken immediately or delayed until the next maintenance interval. In oil and gas production, the production loss consequence is much higher than maintenance expenses, which justify deferred maintenance actions. Estimated current failure rates, e.g., in OREDA, implicitly count human errors in the installation phase, excessive loading and improper operating conditions, periodic inspections and testing, corrective and preventive maintenance interventions, modifications, and life extensions.