Context

The Predictive Maintenance Simulator transforms how operators interpret equipment health by letting them enter key indicators—temperature, vibration, differential pressure—while Copilot automatically classifies asset conditions as normal, warning, or critical. It also provides clear AI‑generated explanations so supervisors can spot recurring issues early. 

Challenges

Equipment health checks often rely on manual judgment, inconsistent interpretation, and delayed escalation. Operators must navigate scattered data, supervisors struggle to connect repeated symptoms, and reliability teams lack unified insights to identify degradation trends. Safety coordination becomes harder without visibility into permit conflicts and risk triggers. 

Solution

Using an AI‑first design, the system leverages Planner to generate the complete blueprint,users, processes, data tables, and apps. Console operators enter indicators, Copilot classifies conditions with reasoning, supervisors review recurring issues, reliability engineers run trend analysis, and safety teams monitor permit‑to‑work conflicts. With structured data, automated health insights, and role‑based apps, the platform delivers faster decisions, early detection, and safer operations.