Data ingestion
Connect MQTT data ingestion, PLC data monitoring, SCADA data analytics, historians, databases, APIs, OPC UA integration, and third-party systems.
EAHM - Enterprise Asset Health Management System
Enterprise Asset Health Management for industrial operations
EAHM is an industrial asset health platform that helps teams monitor, diagnose, predict, and optimize critical assets, utilities, machines, and process systems using digital twin software, predictive maintenance analytics, AI insights, RCA reports, MQTT data ingestion, and customizable dashboards.
Operational problem
Industrial teams need more than plant asset monitoring dashboards
Traditional dashboards show data, but operations and reliability teams still need industrial operations intelligence, diagnostic analytics, root cause analysis software, and a practical path from signal to action.
What changed?
Why did an abnormal condition occur?
Which asset needs attention?
What may fail next?
What action should be taken?
How can teams explain events to management?
Platform overview
One industrial monitoring platform for asset visibility, analytics, AI, and action
EAHM combines industrial data ingestion, asset modeling, digital twin visualization, analytics, AI insights, dashboards, RCA reports, and deployment flexibility into a single asset reliability software layer.
Asset modeling
Create structured asset, equipment, system, and process models that preserve plant and machine context for industrial operations intelligence.
Digital twin
Use an industrial digital twin platform to represent assets visually with operating context, X-ray inspection, digital twin playback, and asset playback mode.
Analytics
Move from descriptive analytics dashboards to diagnostic analytics for assets, industrial predictive analytics, and industrial prescriptive analytics.
AI insights
Generate contextual AI insights, anomaly explanations, failure prediction support, and maintenance decision support from alarms, history, process data, and events.
Dashboards
Build role-specific views for operations, reliability, maintenance, energy, OEM service, and leadership.
RCA reports
Turn abnormal events into structured RCA report software outputs with timelines, contributing factors, AI RCA context, and action guidance.
Deployment flexibility
Support on-premise industrial software, private cloud industrial software, customer cloud, offline industrial analytics, edge analytics, and hybrid architecture needs.
Digital twin
Digital twin with X-ray and playback mode
EAHM provides an industrial digital twin and asset digital twin layer for machines, equipment, utilities, and plant systems. X-ray mode helps users inspect asset behavior and internal relationships. Playback mode helps users review historical behavior, operating conditions, abnormal events, and performance deviations over time.
X-ray mode
Inspect asset behavior, internal relationships, and operating context behind the visible dashboard layer.
Playback mode
Review historical sequences, abnormal events, and performance deviations across time.
Analytics
Analytics across every level of decision-making
EAHM supports descriptive analytics dashboards, diagnostic analytics for assets, predictive analytics for maintenance, and prescriptive maintenance software views so teams can move from event visibility to informed action.
Descriptive analytics
Understand what happened
Review events, health scores, trends, alarms, and operating history.
Diagnostic analytics
Identify why it happened
Connect deviations with asset, process, and machine context.
Predictive analytics
Anticipate what may happen next
Support predictive analytics for maintenance, anomaly detection, and equipment failure prediction before reliability issues grow.
Prescriptive analytics
Recommend what action should be taken
Support prescriptive maintenance software workflows with practical next steps for maintenance and operations teams.
AI with context
AI insights with plant and machine context
EAHM's AI layer can work with plant context, machine context, asset history, alarms, maintenance records, process data, and operating conditions. This helps teams generate industrial anomaly detection, machine anomaly detection, and maintenance decision support insights that are more relevant than generic AI chat.
Why did compressor energy consumption increase last week?
Which pump is showing early degradation signs?
Generate an RCA report for this abnormal event.
Which assets need attention this week?
What changed before the chiller trip?
Dashboards
User-customizable dashboards with rich widgets
EAHM dashboards can be adapted for operations, reliability analytics, industrial energy analytics, maintenance planning, OEM service visibility, and executive review.
KPI cards
Surface critical asset, utility, and process indicators in compact operational tiles.
Trends
Track sensor values, operating conditions, and asset behavior over selected time windows.
Charts
Compare performance, alarms, health, energy, and reliability metrics with visual context.
Asset cards
Show each asset's status, health, alerts, operating state, and recent activity at a glance.
Tables
Review structured readings, event records, alarm lists, and operational data in sortable views.
Alerts
Highlight abnormal conditions, threshold breaches, and assets that need team attention.
Reports
Generate operational, reliability, energy, and RCA summaries for review and communication.
Health scores
Combine signals into clear asset health indicators that support prioritization and planning.
Event views
Inspect event sequences with timestamps, related signals, and supporting operating context.
Performance views
Monitor asset efficiency, runtime, load, deviations, and process performance over time.
Energy views
Analyze energy consumption, losses, cost drivers, and utility performance across assets.
Executive views
Provide leadership with high-level reliability, operations, energy, and action-status visibility.
Data ingestion
Industrial data ingestion designed for real environments
EAHM works as an industrial data platform by connecting machine data monitoring, PLC data, SCADA systems, MQTT industrial IoT feeds, historians, databases, APIs, OPC UA, and existing enterprise systems.
MQTT-based ingestion for industrial IoT software
PLC/SCADA connectivity through gateways
Historian integration and historian data analytics
Database integration
API and OPC UA integration
Third-party system integration
Edge analytics for manufacturing and offline architecture readiness
Deployment
Flexible deployment for secure industrial environments
EAHM can fit customer-controlled environments, secure plant networks, on-premise deployments, private cloud deployments, offline-capable sites, edge systems, and hybrid architecture requirements.
On-premise industrial software
Private cloud industrial software
Customer cloud
Offline-capable industrial analytics
Hybrid architecture
Subscription model
Perpetual license model
EAHM use cases
Relevant use cases across assets, utilities, and machines
EAHM can start with focused needs such as industrial energy monitoring software, air compressor health monitoring, pump predictive maintenance, chiller performance monitoring, CNC machine monitoring software, and injection molding machine monitoring, then expand into a broader asset intelligence platform.
Energy performance insights
Track energy consumption, operating patterns, and abnormal usage across utilities, assets, and plant systems.
Air compressor health
Monitor pressure, load cycles, runtime, temperature, power, and alarms to detect reliability and efficiency issues early.
Pump reliability
Identify early signs of pump degradation, cavitation, blocked flow, operating stress, and abnormal performance.
Boiler health and efficiency
Connect fuel use, steam output, pressure, temperature, water parameters, alarms, and event history for safer review.
Cooling tower performance
Review temperature approach, fan behavior, flow, water quality, power, runtime, and deviations affecting cooling performance.
HVAC health and efficiency
Track temperature, humidity, airflow, runtime, power, setpoints, and alarms across facility systems.
Chiller performance
Monitor chilled water conditions, compressor status, power, runtime, alarms, and abnormal events that affect reliability.
Generator readiness
Check runtime, fuel level, battery condition, load, temperature, test events, alarms, and readiness indicators.
Material handling
Improve visibility into conveyors, motors, load, speed, runtime, stops, alarms, and throughput bottlenecks.
CNC monitoring
Review cycle status, utilization, stops, alarms, spindle load, machine states, and production signals.
Injection molding monitoring
Track cycle time, temperature, pressure, machine states, alarms, production events, and process drift.
MQTT and industrial data
Connect equipment, IIoT gateways, MQTT feeds, historians, PLC and SCADA data, databases, APIs, and third-party systems.
Analytics and RCA
Support reliability analytics software workflows with anomaly detection, event explanation, AI RCA reports, and decision-making context.
Secure flexible architecture
Support on-premise, private cloud, edge analytics, offline-capable, and hybrid deployment patterns.
Explore how EAHM can fit your plant, asset, or OEM requirement
Discuss asset health monitoring software, predictive maintenance software, industrial digital twin, RCA reporting, and industrial operations intelligence requirements with Enserv.