## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**System Diagnostic** & **Value at Time 0 (Initial)** & **Value at Time 1** & **Value at Time 2** & **Value at Time 3** \\ \hline Healthy & 0.9998 & 0.9999 & 0.9999 & 0.9969 \\ Not Healthy & 0.0002 & 0.0001 & 0.0001 & 0.0031 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Progression of system diagnostics following the example accident sequence. * Another set of assumptions present in the case study was the number of expert opinions used to fill the conditional probability tables for events and conditions that did not have available data. The current DBN structure itself is not able to fully incorporate physical knowledge of the system, such as degradation and failure modes, into its diagnostic and prognostic assessments; as such, this system information might be a better alternative for substituting missing data with respect to nodes related to remaining useful life predictions. There has been a range of work in this subject for various systems, including wind turbines [26], railways [27], and subsea pipelines [28];
* A next-level line of study would be to vary the weight of information provided to the system as evidence. Currently, all information used by the model carries the same weight. Certain sensor readings may be more reliable or provide a greater amount of evidence than other sensors; newer system information should take greater precedence in system health assessments than older information;
* Another area worth addressing is the bin size for each of the nodes, particularly those in the system data and system information region. Since the conditional probability tables are dependent upon where state bin thresholds are located, adjusting the bin boundaries will have a significant impact on the conditional probability tables that the model is dependent on. Previous work by [PERSON] and [PERSON] [29] has found attempts to discretize the bins dynamically. This study would also be associated with identifying the appropriate number of bins, based on the findings from [PERSON] and [PERSON] [30]. As an increase in bins allows a greater number of variations, the logical extension of this would be aligned with some of the research that [PERSON] et al. [31], [PERSON] et al. [32], or [PERSON] et al. [33] have performed on continuous-time or hybrid Bayesian networks. Future research from [PERSON] [32] has even suggested creating a continuous-time-based DBN that provides insight over conditional states that are not discrete but based on continuous probabilistic distributions;
* Lastly, a more complicated model could be created by introducing additional accident states to the CES. Having an additional accident state would result in different parameters having different relationships with the same or other relevant system data. Further research is needed to determine how those can be successfully integrated into the model. The direction of where DBN modeling for CES prognostics and diagnostics is heading depends on the priority of the researchers in each application area. Understanding any one of these incomplete research areas better, however, will result in a diagnostic and prognostic model that better reflects the requirement of the CES.