Marine Propulsion Health Monitoring: Integrating Neural Networks and IoT Sensor Fusion in Predictive Maintenance

The maritime sector is shifting towards predictive maintenance to improve marine propulsion system dependability and efficiency. This research introduces neural networks and IoT sensor fusion for marine propulsion health monitoring. Real-time operational data is collected by a sophisticated sensor array spanning crucial propulsion system components. Fusing sensor data using modern IoT algorithms gives a comprehensive overview of system health. The suggested technique uses neural networks for predictive maintenance. A deep learning model analyses sensor-fused data to detect flaws or performance deterioration. Training the neural network on past data from various operating situations allows it to adapt and forecast faults. The model's capacity to learn and develop improves its vessel operating state adaptation. Neural networks and IoT sensor fusion offer early defect identification and dynamic maintenance schedules. Low downtime, operating expenses, and marine propulsion system longevity are achieved using this strategy. Case studies and simulations indicate that the suggested system can predict and avoid significant failures, making it suitable for marine use.