A governed Industrial AI glossary entry for Asset Performance Analytics, covering operational relevance, source records, ambiguity, engine ownership, and product boundary.
This glossary entry defines asset performance analytics and routes readers to AssetMind AI and ReliabilityMind AI for exported-data readiness evidence before claims about asset health, predictive maintenance, or live condition monitoring.
Asset Performance Analytics is the analysis of asset master data, work orders, downtime records, maintenance history, and, where available, condition or historian signals to understand asset health, reliability patterns, and readiness for improvement decisions.
Decision framing, source qualification, evidence generation, confidence review, owner decision, and governed follow-up.
Decision question, operational source, mapped fields, evidence class, confidence tier, owner, review status, action, and audit metadata.
Decision intelligence supports accountable decisions; it does not transfer decision rights to an AI system.
An aviation MRO facility could apply asset performance analytics to engine test cell equipment to identify recurring bearing failures and review whether they concentrate emergency-maintenance cost.
AssetMind AI and ReliabilityMind AI diagnose asset and maintenance evidence from exported EAM or CMMS data. Live sensor, historian, or condition-monitoring analytics require separate connected platforms and are outside the first export-only diagnostic.
Product boundary: AI2COE does not claim live asset monitoring, real-time failure prediction, or historian-based condition analytics unless such data and integrations are explicitly in scope.
Glossary pages are reference entries. They support diagnostic evaluation, but the operating decision still depends on source data, confidence tiers, and accountable owner review.
This public definition is governed by the AI2COE Knowledge Dictionary and should be interpreted alongside the Industrial AI Readiness framework, assessment methodology, and evidence standards.
Use Asset Performance Analytics as a governed reference term, then check the source records, confidence tier, and owner review state before treating it as operational evidence.
Relevant records include: Decision question, operational source, mapped fields, evidence class, confidence tier, owner, review status, action, and audit metadata. The reviewer must also check source context, field meaning, and the stated ambiguity before action.
Decision intelligence supports accountable decisions; it does not transfer decision rights to an AI system.
AssetMind AI is the primary engine. Supporting engines are used only when their evidence is substantively relevant: ReliabilityMind AI, ReadyMind AI.
AI2COE does not claim live asset monitoring, real-time failure prediction, or historian-based condition analytics unless such data and integrations are explicitly in scope.