LineGuard: Predictive Maintenance Copilot on AMD

Factories already collect sensor data, but their alarms fire only when a fixed limit is crossed, typically about 10 hours before a machine trips. That is too late to order the part and plan the stop, so the repair happens as a breakdown during production. LineGuard learns each machine's normal behaviour for every hour of the shift and flags small, persistent drifts toward a protection limit. It matches the pattern to known failure modes (e.g. bearing wear, cooler fouling) and predicts when the machine will trip by fitting the accelerating trend forward. An AI copilot, an open model (Qwen3.8-Flash-Next) running on AMD GPUs via AMD Token Factory, turns each alert into a plan an operator can follow: what is happening, what to check, which part, how long, the safety steps and a deadline. Every statement cites the plant's own manuals, safety procedures and past work orders. A planner approves the generated work order into a shift-change slot, and the replay shows the machine repaired in a planned stop instead of breaking down. On 50 unseen simulated fault scenarios (149 faults), LineGuard warned a median of 33 hours before the trip, against 10 hours for fixed alarms, with no false alerts on healthy machines. One day before a trip, its time-to-trip prediction was within about ±15%. Under explicit example cost assumptions, it avoids about €18.7k and 3 hours of downtime per fault. Detection and all numbers are computed by deterministic code. The language model only explains and plans; its citations are checked and lockout/tagout is always enforced.