## 4 Applications of Multimodal Learning for Fault Detection Diagnosis
### Applications in Aerospace Systems  
Aerospace systems including aircraft and spacecraft are highly complex and require continuous monitoring to ensure safe and efficient operation. Fault detection and diagnosis (FDD) are essential for maintaining system reliability, minimizing downtime, and preventing catastrophic failures. The adoption of advanced machine learning (ML) and deep learning (DL) techniques has significantly improved the accuracy and speed of fault detection in aerospace applications. These technologies facilitate the integration of data from multiple sensors embedded in engines, structural components, and subsystems, thereby enhancing the overall diagnostic process. One key area of fault detection in aerospace is monitoring aircraft engines. Since engines are critical to aircraft safety and performance, the early detection of faults is essential. [PERSON] et al. (2021) utilized convolutional neural \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Application** & **Paper** & **Year** & **Technique Used** \\ \hline Autonomous Vehicle Fault Detection & [PERSON] et al. & 2020 & \
\begin{tabular}{c} MSALSTM-CNN: Hybrid Learning \\ (CNN + LSTM) [79] \\ \end{tabular} \\ Electric Vehicle Battery Health Monitoring & [PERSON] et al. & 2022 & Support Vector Machine (SVM) [80] \\ Engine Fault Diagnosis & [PERSON] et al. & 2024 & Decision Trees (DT) [77] \\ Battery Monitoring for EVs & [PERSON] et al. & 2024 & Random Forest (RF) [81] \\ Autonomous Vehicle Sensor Fusion & [PERSON] et al. & 2022 &
\begin{tabular}{c} Multimodal Learning \\ (Fusion of Radar + Lidar) [82] \\ \end{tabular} \\ Predictive Maintenance for EV Batteries & [PERSON] et al. & 2024 & Deep Neural Networks (DNNs) [83] \\ Autonomous Vehicle Fault Detection & [PERSON] et al. & 2021 & Recurrent Neural Networks (RNNNs) [84] \\ Engine Performance Monitoring & [PERSON] et al. & 2023 & Gradient Boosting Machines (GBMs) [85] \\ Battery Fault Detection in EVs & [PERSON] et al. & 2022 & Convolutional Neural Networks \\  & & & (CNNs) [86] \\ Vehicle System Health Monitoring & [PERSON] et al. & 2022 & Multilayer Perceptron (MLP) [87] \\ Autonomous Vehicle Navigation Faults & [PERSON] et al. & 2023 & Long Short-Term Memory (LSTM) [88] \\ EV Battery Fault Diagnosis & [PERSON] et al. & 2024 & K-Nearest Neighbor (KNN) [89] \\ Vehicle Diagnostics in Autonomous Driving & [PERSON] et al. & 2020 & Transfer Learning [90] \\ Electric Vehicle Fault Detection & [PERSON] et al. & 2024 & Naive Bayes Classifier (NBC) [91] \\ Autonomous Vehicle Fault Classification & [PERSON] et al. & 2020 & Deep Learning (DL) [92] \\ Engine Fault Classification & [PERSON] et al. & 2022 & Extreme Learning Machine (ELM) [93] \\ Battery Performance Degradation Detection & [PERSON] et al. & 2022 & Gaussian Process (GP) [94] \\ Vehicle Engine Health Monitoring & [PERSON] et al. & 2021 & Multiscale Learning (MSL) [95] \\ Vehicle Fault Detection using Sensor Data & [PERSON] et al. & 2022 & Multi-Task Learning (MTL) [96] \\ Autonomous Vehicle Sensor Reliability & [PERSON] et al. & 2023 & Random Forest (RF) [97] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Presents multimodal learning applications in automotive contexts. networks (CNNs) to detect faults in aircraft engines. Their model, trained on large-scale sensor datasets, demonstrated the ability to assess engine health in real time and identify anomalies before they escalate [41]. Another significant area is the monitoring of aircraft power systems, which supply electrical energy for essential flight operations. Faults in these systems can disrupt mission-critical functions. [PERSON] et al. (2020) used support vector machines (SVMs) to monitor key parameters such as voltage, current, and frequency, identifying patterns that indicate potential faults. Their model achieved high diagnostic accuracy and provided actionable alerts for maintenance teams [98]. Monitoring the structural integrity of aircraft is equally important. Components such as wings and the fuselage endure stress and fatigue over time. [PERSON] et al.