Industrial DataOps
The practice of connecting, conditioning, modeling, and governing industrial data so analytics, AI, digital twins, and operations systems can trust it.
Related TwinEdge page →Resources
Asset management, CMMS, EAM, and industrial AI glossary
Plain-language definitions for asset management, CMMS, EAM, asset intelligence, predictive analytics, predictive maintenance, and related TwinEdge software categories.
Unified Namespace (UNS)
A structured, shared industrial data model—often over MQTT/Sparkplug—where systems publish and subscribe to consistently named topics instead of building one-off point-to-point integrations.
Related TwinEdge page →Asset management
The practice of managing physical equipment through its life — identity, condition, maintenance, cost, risk, and replacement — so operations can keep assets reliable without overspending.
Related TwinEdge page →Asset management and maintenance
The combined work of knowing the asset and keeping it in service: hierarchy, condition, work orders, parts, technicians, and closeout on one operating record.
Related TwinEdge page →CMMS
Computerized Maintenance Management System software for work orders, preventive maintenance, inventory, and maintenance history.
Related TwinEdge page →EAM
Enterprise Asset Management software that extends CMMS into broader asset lifecycle, hierarchy, reliability, procurement, and capital context.
Related TwinEdge page →Asset intelligence
A live, explainable picture of an asset: identity, operating history, condition, risk, recommended work, and evidence — not a static register or a disconnected dashboard.
Related TwinEdge page →Predictive analytics
Analytics that estimates likely future equipment behavior from live condition, operating context, models, and history, then presents a reviewable finding instead of a raw alert.
Related TwinEdge page →Predictive maintenance
Maintenance planning based on estimated future degradation or failure risk using condition data, models, and history—not calendar intervals alone.
Related TwinEdge page →Condition-based maintenance
Maintenance or inspection triggered by observed asset condition and operating evidence rather than elapsed time only.
Related TwinEdge page →Physics-based digital twin
A live operational model that evaluates equipment and process behavior using first-principles relationships, envelopes, and telemetry—not only visualization.
Related TwinEdge page →Governed industrial AI agent
An AI workflow that can observe operational context, draft recommendations, and request human approval while preserving evidence, audit, and policy bounds.
Related TwinEdge page →TwinEdge OS
TwinEdge edge runtime for industrial protocol collection, local storage, dashboards, alerts, and supported on-site AI inference.
Related TwinEdge page →TwinEdge Platform
TwinEdge cloud and hybrid coordination layer for DataOps, digital twins, agents, AssetOps, Field, compliance, and multi-site operations.
Related TwinEdge page →MCP (Model Context Protocol)
A protocol for exposing governed tools and context to AI clients. In TwinEdge, MCP access is cataloged, tenant-scoped, and read-only by default.
Related TwinEdge page →Edge-to-cloud architecture
A hybrid design where time-sensitive collection and local intelligence run near equipment while fleet analytics and enterprise workflows can run in the cloud.
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