Compressor Maintenance Copilot

Compressor Maintenance Copilot watches compressor sensor data, warns before a fault stops the machine, and turns each warning into something a maintenance lead can act on. A statistical detector learns what normal looks like from two months of real data (UCI MetroPT-3, a metro train's air compressor) and is never tuned on failures. On the four air leaks recorded later that year it warned before three of them, 14 hours to more than 2 days ahead, with about one false-alert episode per week. Each alert goes to an Evolus workflow. Its Evolus agent thinks with DeepSeek-V4-Flash running on AMD GPUs through AMD Token Factory, connected as our own model, and searches an Evolus knowledge library of the compressor manual and past work orders. It explains what is happening in plain language, names the likely cause, lists the checks to run, cites its sources, and drafts the work order with parts to have ready. The technician can then ask follow-up questions in chat, such as what was done last time. Safety rules are enforced in code, not only in the prompt: every citation must be a real document, any "safe to keep running" verdict is removed because that decision belongs to the maintenance lead, and low confidence or an unreadable answer escalates to a person. A second machine (AI4I 2020 milling data, with per-failure-mode classifiers) shows that the same flow generalises. Built with Python, scikit-learn, Streamlit, the Evolus REST API (agent, knowledge library, workflow) and AMD Token Factory. Open source under MIT; the manual and work orders are synthetic and labelled so.