Project Overview
Developed for Precision Manufacturing Co., this IoT and machine learning system monitors equipment health in real-time and predicts maintenance needs before failures occur.
Challenge
The manufacturing facility was experiencing frequent unplanned downtime, high maintenance costs, and production delays due to equipment failures.
Solution
We deployed IoT sensors across critical machinery and developed time-series forecasting models to predict equipment failures with 94% accuracy 7-14 days in advance.
Technology Stack
- IoT sensors and edge computing devices
- Python with Prophet and LSTM networks
- Azure IoT Hub for data collection
- Power BI for real-time dashboards
- REST APIs for maintenance workflow integration
Results & Impact
The system monitors 200+ pieces of equipment across 3 manufacturing plants and has transformed maintenance from reactive to predictive, saving millions in operational costs.