## 4 Discussion  
The results indicate that the DBN model provided a system diagnostic and prognostic capability for the reactor accident sequences that it was designed to monitor. Using available information from a large number of system sensors, a clearer image of the current and future system health was estimated for a complex system. The strength of the model lies in its inference abilities, as it provides a responsive posterior probability for both specific system outcomes and current health and accident states. This type of modeling is important to consider when monitoring CES health, as it provides a visually appealing method of presenting the causal relationships found in these systems and subsystems. CESs have heavily integrated platforms that, through other techniques, would otherwise not have their time-dependent causal relationships truly defined. As this capability is possible with DBN models, there are now better and more accurate PHM strategies that are available to use on CESs than previously utilized. One of the common challenges associated with applying DBNs to real systems is the CPT quantification process. Depending on the number of state bins and the amount of parents for each node, the size of the tables can vary greatly, increasing the time and power required to process the probabilities. For this case study, most failure scenarios led to the same parent/child node relationships and some parent/child combinations were not met. Limiting the number of accident scenarios, minimizing the amount of states per node, and relying on expert-based relationships may reduce the computational requirements; however, an increase in the number of scenarios and state bins make the model applicable to a wider range of accidents and failure modes and increases the granularity of the model, respectively. An analysis is needed to identify the proper amount of granularity and model coverage for each specific CES. The current structure of the DBN model is designed for a continuously operating CES that can experience an accident at any given moment. Given the long operational lifetime of these systems relative to the start-up and wind-down time periods, this is a reasonable assumption; however, accidents can just as easily occur at the onset of operation or operation build-up. To consider these time periods when constructing a model, data or expert-based opinions of the system relationships are needed. Such a model may end up entirely distinct from one of a similar CES in its operational phase. As this modeling approach is intended to work for all CESs that experience accident events, there are many areas of improvement for this type of work to reflect the diversity of its application areas:  
* First, further study is needed on the impact that the time step measurements at which data are received have on CES prognostics and diagnostics assessments. In the SFR case study, the CPTs were designed with data collected over varying lengths of time but assumed to be equal evidence. Constructing the CPTs over simulation time, rather than data collection time, may result in a different assessment of system health. An initial study by [PERSON] and [PERSON] looked at the potential impacts that different time discretization techniques had on prognostics and diagnostics performance. The SFR case study could be used as a case study to quantify some of these differences [25];
* One major assumption in constructing the operational timelines for different accident scenarios was that every piece of system information not received at that point in time was assumed to be constant until otherwise changed. Additional work is needed to identify other methods of unmeasured system data evolution, as it is possible that other predictive approaches, such as filtering or forecasting, may ultimately be used to provide a more accurate estimate of certain values before the new values become known;