About this project
Problem: Manufacturers run one of two costly maintenance strategies. Reactive maintenance fixes machines only after failure, causing expensive unplanned downtime. Fixed-schedule maintenance services machines on a calendar regardless of actual condition, wasting money on machines that are still healthy. Predictive maintenance — servicing a machine exactly when its sensor data says it needs it — solves both, but normally requires a human to notice an anomaly, then separately estimate its severity, then separately request a repair recommendation. Solution: UptimeAgent removes those manual hops by exposing three MCP tools designed to be chained autonomously by an LLM agent in a single turn. analyze_sensor_reading compares a machine's recent sensor readings against its own healthy baseline to detect anomalies. predict_failure_window fits a trend line to project remaining useful life in cycles/days, with a confidence score. generate_work_order combines both analyses with a maintenance-manual lookup to produce an actionable ticket: urgency level, issue description, recommended repair action, and estimated remaining life. Asking the agent "How's Engine 3 doing?" triggers all three tools in sequence, unprompted, ending in a finished maintenance ticket.
Manufacturing & Industry 4.0 track
Create intelligent systems for smart factories, predictive maintenance, quality control, and supply chain optimization.