## 4 IDAIC Framework
### Invisible Problem Space  
#### 4.2.1 Domain Exploration  
Once the system is re-commissioned or retro-commissioned, the invisible problem-solving activity is enabled. The outer ring in Figure 8 aims to explore the data to detect early signs of degradation that would inform decision makers on the remaining useful life of components to plan maintenance activities accordingly. The first phase is to determine if the current problem understanding is sufficient. To do this, exploration of the domain is undertaken to establish if the system is operating in a stable and efficient manner. While the operation assessment aims at detecting and diagnosing visible problems, the domain exploration aims at assessing the efficiency of the system and propositioning the areas where data-driven analysis may provide value. For example, the operation assessment may identify a passing valve that is then replaced in the commissioning phase. However, this valve may have reached the end of its useful life prematurely, resulting in costly replacement and insufficient knowledge as to the cause of the rapid degradation of this valve. In this scenario, data-driven analysis may provide insight into the degradation pattern of the new valve, creating valuable information for the facilities management team that enables smarter, proactive decision making. In collaboration with the domain expert, the features of interest may be targeted for analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Strength** & & **Weakness** \\ \hline \multirow{4}{*}{Knowledge-based FDD} & \multirow{4}{*}{\(\bullet\)} & The detailed mathematical model is not & \(\bullet\) & It highly relies on domain expertise while a wide range of faulty and failure cases are beyond engineers’ experiences \\  & & needed available & \(\bullet\) & Not capable of detecting novel failures which are not flagged in the historical data \\  & & inputs, outputs, and states & \(\bullet\) & Cannot work well for complex and large-scale HVAC systems \\ \hline \multirow{4}{*}{Model-based FDD} & \multirow{4}{*}{\(\bullet\)} & Works properly when a good HVAC physical model is available & \(\bullet\) & It is not proper for large-scale and complex HVAC systems \\  & & Less model development time and cost & \(\bullet\) & The performance would be limited because of modeling and linearization error \\ \hline \multirow{4}{*}{Data-driven based FDD} & \multirow{4}{*}{\(\bullet\)} & Less model development time and cost & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{Need to collect the high quality and sufficient training data for supervised models} \\  & & No dependency on the model & & \\ \cline{1-1} \cline{3-3}  & Easy to retain & & & \\ \cline{1-1} \cline{3-3}  & & Efficient use of system data & & \\ \cline{1-1} \cline{3-3}  & & High accuracy & & \\ \cline{1-1} \cline{3-3}  & & Good performance in works with large-scale and complex systems & & \\ \cline{1-1} \cline{3-3}  & & Minimizing cognitive errors which are beyond the engineers’ knowledge & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of strengths and weaknesses of HVAC FDD methods. Reprinted from [PERSON] and [PERSON] [26], with permission from Elsevier, 2020.