Cloud-Enabled Predictive Vehicle Health Monitoring and Maintenance with LSTM Networks for Time-Series Analysis

Maximizing operational efficiency and customer satisfaction in the automobile sector depends on maintaining vehicle health and avoiding maintenance downtime. Vehicle Internet of Things (IoT) devices capture massive volumes of sensor data, enabling predictive maintenance. This research offers cloud-based predictive vehicle health monitoring and maintenance systems using Long Short-Term Memory (LSTM) networks for time-series analysis. System architecture uses IoT sensors in automobiles to gather real-time data on engine temperature, oil pressure, and vibration. This data is stored, analyzed, and predicted on clouds. To assess time-series sensor data and forecast failures or maintenance requirements, LSTM networks, a form of recurrent neural network, are used. The proposed system uses data preprocessing to manage missing values and outliers, feature extraction to find significant sensor data patterns, and LSTM-based predictive modeling to predict vehicle health conditions. Technology may predict future faults by continually monitoring and evaluating the vehicle's state, allowing proactive maintenance to avoid expensive breakdowns and downtime. The cloud-based architecture is scalable and flexible, providing smooth connection with fleet management systems and remote vehicle health monitoring across scattered sites. The suggested method optimizes maintenance schedules and resource allocation using predictive insights from real-time sensor data to improve operational efficiency and save costs. It may improve predictive vehicle health monitoring and maintenance in the automobile sector, improving dependability, safety, and cost.