## 3 Materials and Methods  
The proposed methodology is presented in Figure 3. The complete fault detection and classification framework can be viewed as a combination of three major segments: multi-step prediction, fault detection, and fault classification. We consider a fault detection and classification framework for a single node in a sensor network. The multi-step prediction segment forecasts multiple future time steps, referred to as the prediction window. This segment is implemented using an LSTM autoencoder architecture. As shown in the figure, the LSTM autoencoder takes \(N\) time steps as input, from time instance \(t\) to \(t-N\) as input. It then predicts \(M\) future time steps, from \(t\) to \(t+M\). In the next step, features selected during the feature-selection stage are computed from the predicted time steps. The prior feature-selection stage ensures that the most important features are chosen to minimize computational load during the fault-detection and classifi \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Dataset** &
\begin{tabular}{c} **Time Series** \\ **Type** \\ \end{tabular} & **Model** & **Features** & **Task** \\ \hline
[16] &
\begin{tabular}{c} Temperature signal collected \\ by Arduino \\ \end{tabular} & Univariate & SVM & Statistical features & Classification \\ \hline
[17] & Gas turbine sensor data collected \\ from simulator & Univariate & SVM & EMD based features & Classification \\ \hline
[18] & Sensor data published by Intel lab & Multivariate & \
\begin{tabular}{c} SVM with Grey-Wolf \\ Optimization \\ \end{tabular} &
\begin{tabular}{c} Feature extracted by \\ Kernel Principle \\ Component Analysis \\ \end{tabular} & Classification \\ \hline
[19] &
\begin{tabular}{c} Various sensor data collected from \\ pressurized water reactor \\ \end{tabular} & Univariate & SVM, KNN, NN & - & Classification \\ \hline
[21] & \
\begin{tabular}{c} Temperature and humidity sensor \\ signal from a wireless sensor \\ network setup \\ \end{tabular} & Multivariate &
\begin{tabular}{c} SVM, RF, DT, \\ Extra-Trees \\ \end{tabular} & - & Classification \\ \hline
[22] & \
\begin{tabular}{c} Altitude barometer sensor signal \\ collected from an UAV \\ \end{tabular} & Univariate & CNN &
\begin{tabular}{c} Time frequency \\ images \\ \end{tabular} & Classification \\ \hline
[23] &
\begin{tabular}{c} Various sensor data collected from \\ aeroengine control system \\ \end{tabular} & Univariate & CNN & CWT scalograms & Classification \\ \hline
[33] &
\begin{tabular}{c} Sensor signal from semiconductor \\ manufacturing process \\ \end{tabular} & Univariate & VAE & - & Classification \\ \hline
[34] & \
\begin{tabular}{c} Synthetic data generated from \\ shear-type structure and experimental \\ data from an arc bridge structure \\ \end{tabular} & Multivariate & CNN and CAE & - &
\begin{tabular}{c} Detection, \\ classification, and \\ correction \\ \end{tabular} \\ \hline
[44] & Sensor data derived from autonomous \\ driving dataset & Univariate & 1D CNN and DNN & \
\begin{tabular}{c} Time domain \\ statistical features \\ \end{tabular} &
\begin{tabular}{c} Detection, \\ classification, and \\ isolation \\ \end{tabular} \\ \hline
[45] & \begin{tabular}{c} Sensor data from air quality dataset, \\ WSN dataset, and Permanene Magnet \\ Synchronous Dataset \\ \end{tabular} & Multivariate & \
\begin{tabular}{c} ANN based digital \\ twin concept \\ \end{tabular} & - &
\begin{tabular}{c} Detection, \\ classification, and \\ accommodation \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of approaches for sensor fault analysis in the existing literature. cation stages. Once these key features are computed, the feature vector is provided to the first-stage classifier, which performs fault detection. If the classifier identifies a potential fault, the features are then utilized by a second-stage classifier to classify the type of fault.