Development of AI-Driven Predictive Maintenance System for Electronics Manufacturing

Our electronics manufacturing company is facing challenges with unexpected equipment failures that lead to costly downtime and production delays. To address this, we aim to develop an AI-driven predictive maintenance system. This system will utilize advanced machine learning algorithms to analyze sensor data from manufacturing equipment and predict potential failures before they occur. The project involves building a solution that combines computer vision and predictive analytics to monitor equipment health in real-time. Key components include data collection from IoT sensors, developing ML models using TensorFlow or PyTorch, and integrating these models with our existing manufacturing systems. The project will also involve leveraging the OpenAI API for enhanced data processing and Langchain for creating automated maintenance workflows. Our target completion of this project is within a 12-16 week timeframe, with a budget allocation of $50,000 to cover development and integration costs.