AI-Driven Predictive Maintenance System for Fleet Optimization

In the trucking and delivery industry, vehicle downtime can significantly impact business operations, leading to delayed deliveries and increased operational costs. Our startup is looking to address this issue by developing an AI-driven predictive maintenance system. The project involves using machine learning algorithms, particularly LLMs and predictive analytics, to analyze historical maintenance data alongside real-time vehicle sensor data. The goal is to predict potential vehicle failures and schedule maintenance proactively. The system will utilize key technologies such as OpenAI API for natural language processing, TensorFlow for model training, and edge AI to process data directly on vehicles. The implementation will encompass developing a robust machine learning model, setting up a scalable infrastructure for data processing, and integrating the solution with existing fleet management systems. This will enhance fleet reliability, reduce maintenance costs, and ensure timely delivery, giving us a competitive edge in the market.