## 5 Conclusions
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Method** & **Real-Time Capability** & **Effectiveness** & **Computational Complexity** & **Characteristics** & **Fault Detection/Isolation/Identification** \\ \hline \multirow{4}{*}{Least Squares (LS) [18, 32, 38]} & \multirow{4}{*}{Offline} & \multirow{4}{*}{Effective for static faults} & \multirow{4}{*}{Low} & Not suitable for real time; sensitive to noise; noise; poor performance under transients & Detection only \\ \cline{1-1} \cline{5-5}  & & & & & Sensitive to noise and & \\ \cline{1-1} \cline{5-5}  & & & & & & Detection + Identification \\ \hline \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{Real time} & \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{Moderate} & Not suitable for real time; sensitive to noise; noise; poor performance under transients & Detection only \\ \cline{1-1} \cline{5-5}  & & & & & Sensitive to noise and \\ \cline{1-1} \cline{5-5}  & & & & & & Detection \\ \cline{1-1} \cline{5-5}  & & & & & & Identification \\ \hline \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{Real time (with tuning)} & \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{High} & Requires accurate model; sensitive to noise covariance; tuning required & Full (Detection, Isolation, Identification) \\ \cline{1-1} \cline{5-5}  & & & & & & Isolation, Identification \\ \cline{1-1} \cline{5-5}  & & & & & & Identification \\ \hline \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{Offline} & \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{Very High} & Requires symbolic derivation; not robust to noise & Full (Detection, Isolation, Identification) \\ \cline{1-1} \cline{5-5}  & & & & & & Identification \\ \cline{1-1} \cline{5-5}  & & & & & & Identification \\ \hline \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{Offline (train-selection, parameter tuning)} & \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{High} & Requires symbolic derivation; not robust to noise & Full (Detection, Isolation, Identification) \\ \cline{1-1} \cline{5-5}  & & & & & & Identification \\ \cline{1-1} \cline{5-5}  & & & & & & Identification \\ \hline \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{Offline (train-selection, parameter tuning)} & \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{High} & Not suitable for real time; used primarily for offline training/optimisation & Detection, Optimisation (offline) \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & & \\ \hline \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{Possible (inference only)} & \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{Moderate–High} & Requires offline; real time only & Full (if combined with logic/rules) \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{Real-time selected Jacobian} & \multirow{4}{*}{Real-time selected to noise; suited for abrupt faults;} & \multirow{4}{*}{Low} & Performance depends on window size; & Full (Detection, Isolation, Identification) \\ \cline{1-1} \cline{5-5}  & & & & & & Identification \\ \cline{1-1} \cline{5-5}  & & & & & & Identification \\ \cline{1-1} \cline{5-5}  & & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & & Identification \\ \hline \hline \end{tabular}
\end{table} 
Table 21: Comparison of Proposed FDII Framework with Existing Literature.