## 4 Applications of Multimodal Learning for Fault Detection Diagnosis
### Applications in Renewable Energy Systems
#### 4.2.1 Wind Turbine Fault Diagnosis  
Wind turbines are complex machines with numerous mechanical components, each of which can degrade or fail over time. Early fault detection is crucial for avoiding costly repairs and reducing downtime. Typically, wind turbines are equipped with sensors  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Application** & **Paper** & **Year** & **Technique Used** \\ \hline Vehicle Fault Detection & [PERSON] et al. [48] & 2023 & Convolutional Neural Networks (CNNs) \\ Automotive System Monitoring & [PERSON] et al. [49] & 2024 & Long Short-Term Memory (LSTM) \\ Engine Fault Diagnosis & [PERSON] et al. [50] & 2022 & Support Vector Machines (SVMs) \\ Brake System Failure Detection & [PERSON] et al. [51] & 2024 & Random Forest (RF) \\ Vehicle Battery Health Monitoring & [PERSON] et al. [52] & 2020 & Decision Trees (DTs) \\ Transmission Fault Detection & [PERSON] et al. [53] & 2020 & K-Nearest Neighbors (KNN) \\ Vehicle Condition Monitoring & [PERSON] et al. [54] & 2022 & Deep Neural Networks (DNNs) \\ Hybrid Vehicle Fault Diagnosis & [PERSON] et al. [55] & 2021 & Multi-Layer Perceptron (MLP) \\ Fault Detection in Electric Vehicles & [PERSON] et al. [56] & 2020 & Naive Bayes Classifier (NBC) \\ Electric Motor Fault Diagnosis & [PERSON] et al. [57] & 2021 & Extreme Learning Machine (ELM) \\ Engine Vibration Monitoring & [PERSON] et al. [58] & 2020 & Gaussian Process (GP) \\ Vehicle Powertrain Fault Detection & [PERSON] et al. [59] & 2021 & Artificial Neural Networks (ANNs) \\ Hybrid Electric Vehicle Fault Diagnosis & [PERSON] et al. [60] & 2022 & Transfer Learning (TL) \\ Automotive Sensor Fault Detection & [PERSON] et al. [61] & 2020 & Recurrent Neural Networks (RNNs) \\ Vehicle Vibration Diagnosis & [PERSON] et al. [62] & 2021 & Deep Belief Networks (DBNs) \\ Autonomous Vehicle Fault Detection & [PERSON] et al. [41] & 2021 & Multimodal Feature Fusion \\ Driver Behavior Prediction & [PERSON] et al. [63] & 2020 & Reinforcement Learning (RL) \\ Vehicle Monitoring in Smart Cities & [PERSON] et al. [64] & 2020 & Hybrid Machine Learning Models \\ Fault Detection in Autonomous Cars & [PERSON] et al. [65] & 2022 & Multi-Task Learning (MTL) \\ Battery Management System Faults & [PERSON] et al. [66] & 2020 & Deep Convolutional Networks (DCNs) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summarizes the applications of machine learning techniques in automotive systems.  
that monitor key parameters such as vibration, temperature, and acoustic emissions [68]. Analyzing these data sources enables early identification of mechanical issues before significant damage occurs.  
Vibration sensors are especially effective at diagnosing misalignments or gear wear, whereas acoustic sensors can detect early signs of cracks or other structural faults [69]. By integrating data from multiple sensors, diagnostic systems obtain a more comprehensive view of turbine health, thus enhancing the accuracy and reliability of fault detection.  
ML techniques are essential for processing sensor data and identifying fault patterns. Algorithms such as decision trees (DTs), support vector machines (SVMs), and deep neural networks (DNNs) classify fault types. For instance, DNNs excel at identifying patterns from vibration data, while long short-term memory (LSTM) networks track temporal changes that indicate gradual deterioration.  
Recently, hybrid models that combine convolutional neural networks (CNNs) and LSTMs have demonstrated great promise. CNNs are well suited to extracting spatial features, like the frequency-domain characteristics of vibration signals, while LSTMs excel at modeling time-dependent patterns [41]. Together, these models provide a robust framework for high-accuracy fault diagnosis in wind turbines.