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SYSTEM: OPERATIONAL OT/IT CONNECTORS: 150+ AUTONOMOUS OPERATION: 15+ DAYS GOVERNED AUTONOMY: ENFORCED AUDIT TRAIL: IMMUTABLE INDUSTRIES: ASSET-INTENSIVE & MISSION-CRITICAL DEPLOYMENT: 3-6 MONTHS VIA APEX CONTROL LOOPS: 3,400+ SYSTEM: OPERATIONAL OT/IT CONNECTORS: 150+ AUTONOMOUS OPERATION: 15+ DAYS GOVERNED AUTONOMY: ENFORCED AUDIT TRAIL: IMMUTABLE INDUSTRIES: ASSET-INTENSIVE & MISSION-CRITICAL DEPLOYMENT: 3-6 MONTHS VIA APEX CONTROL LOOPS: 3,400+
Available CORE-EQUIP-MON-AGT-001 AI Agent

Agentic Equipment Monitoring Agent (Health Monitor)

Provides continuous, intelligent equipment health assessment across entire asset fleets by fusing multi-parameter sensor data with Composite AI to deliver prioritised, contextualised health alerts that operators can trust — eliminating alarm floods and enabling genuinely predictive maintenance.

ManufacturingMiningOil & GasEnergy & UtilitiesWater & Wastewater Asset Monitoring

Target outcome · Equipment anomalies detected 70% earlier than threshold-based monitoring, nuisance alarms reduced by 80%, and correct root cause identified in 85% of detected anomalies.

Business problem

Manufacturing and industrial facilities face an escalating crisis in equipment health management. Equipment health deteriorates gradually, with subtle multi-parameter changes that go unnoticed until failure is imminent. Critical anomalies are buried in millions of sensor readings across hundreds of assets, and traditional threshold-based monitoring generates alarm floods that overwhelm operators — causing alert fatigue and the very failures operators are trying to prevent.

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Diagnosing issues requires correlating multiple data streams across vibration, temperature, pressure, electrical, and process parameters in ways that exceed human pattern recognition capacity. Manual diagnostic processes are too slow to prevent cascading failures in interconnected systems. By the time anomalies are detected through manual review, damage has often already begun and the critical intervention window has closed.

What it does

The Equipment Monitoring and Diagnostics Agent is an autonomous Decision Agent that continuously processes multi-parameter sensor data using Composite AI — combining sensor data analysis, pattern recognition, diagnostic rule sets, and machine learning.

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It distinguishes genuine anomalies from normal process variation, provides prioritised health alerts with diagnostic reasoning and confidence scores, and triggers condition-based work orders in CMMS systems — all within an alarm management framework that respects ISA-18.2 standards and prevents operator overload.

Current process vs. with AI Agent

TODAY · ASSET MONITORINGREACTIVE
×
Anomaly detectionStatic thresholds on individual parameters; anomalies missed until failure is imminent
×
Alert prioritisationAll alarms treated equally; hundreds of alerts per shift overwhelming operators
×
Diagnostic assessmentManual investigation by reliability engineers taking hours; correct root cause found inconsistently
×
CMMS work order initiationManual work order creation after operator identifies issue; delay between detection and action

Outcomes and measurement

Anomaly detection lead time

Baseline Detected at or near point of failure
With agent 70% earlier detection versus threshold-based monitoring

Nuisance alarm rate

Baseline High false-positive volume causing alert fatigue
With agent 80% reduction through intelligent filtering and context-aware alarm management

Diagnostic accuracy

Baseline Inconsistent manual root cause identification
With agent Correct root cause identified in 85% of detected anomalies

Response time to critical anomalies

Baseline Hours to days from detection to maintenance intervention
With agent Minutes from detection to prioritised alert and CMMS work order

*All figures are typical ranges. Achievable range depends on existing control maturity, data quality, and site-specific conditions.

Data inputs

Other

Ingests real-time sensor data via XMPro Data Stream Designerincluding vibration spectrapressure measurementsflow ratesprocess variablesnormal operating envelopesmaintenance historyand current operating context from SCADA and CMMS systems

temperature readings

bearingsmotor windingsoil

electrical parameters

currentvoltagepower factor

equipment specifications

*Categories only — no tag names or system-specific field references. Exact data mapping is scoped per site.

Scoping questions

Expect these questions in a first scoping conversation. They signal engineering discipline and help narrow the template to your specific site context.

  1. Which equipment assets are highest priority for continuous monitoring, and what sensor types are currently available for each?
  2. What are your current alarm management policies, and what alarm rate limits should the agent respect to prevent operator overload?
  3. Do you have existing condition monitoring systems (vibration analysers, thermal imaging, oil analysis labs) that need to be integrated as data sources?
  4. What CMMS or EAM platform needs to receive condition-based work orders generated by the agent?
  5. What is the acceptable confidence threshold for autonomous alert generation versus recommendations that require diagnostic engineer review?

Want our AI to walk you through these scoping questions?

SPEAK WITH OUR TEAM

Get specialist advice on scoping this for your site.

Our specialists will help you understand how the Agentic Equipment Monitoring Agent (Health Monitor) fits your operations, what data you'd need, and what a scoping engagement typically looks like.

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