Challenge
Critical equipment reliability depended on reactive maintenance, fragmented records and limited visibility into asset condition.
Unify asset management, work orders, condition monitoring, preventive and predictive maintenance with AI to detect issues earlier, diagnose faster and keep production running.
Move beyond reactivity: connect asset health with operational context to eliminate unplanned downtime, optimize technician workflows, and shift from emergency repairs to predictive precision.
Combine condition data, maintenance history, manuals and production context to detect issues early, diagnose faster and recommend the right actions.
Find abnormal asset behavior.
Understand root causes.
Suggest next best actions.
Create work orders and orchestrate workflows.
Vibration on the drive-end bearing has increased above its baseline over the last 36 hours. A similar pattern preceded a bearing replacement on Aug 12, 2024.
Asset status and condition data
Identify anomalies and maintenance needs
Analyze with AI and asset context
Assess criticality and production impact
Schedule and allocate resources
Guide technician with digital/AR instructions
Confirm asset condition and return to service
Capture outcomes and improve reliability
C-102 • Centrifugal Compressor
Critical • Plant 1 / Utility
Line: Utilities
Product: Plant Air
Status: Running
BOM: 245 items
Spares: 12 in stock
Last: Jun 2024
Runtime: 14,100 hrs
Cycles: 468,240
Load: 78%
Vibration: 6.8 mm/s
Temp: 78 °C
Pressure: 6.2 bar
Next PM: 31,760 cycles
Last PM: Sep 18, 2025
Status: On schedule
Fix after failure
Breakdown → Repair
Maintain on schedule
Time • Runtime • Cycles
Maintain on changes
Sensors • Thresholds
Anticipate failures
Context • AI Workflow
QR / RFID
Status & history
Ask AI or guides
AR Instructions
Photos & notes
Work order log
Know tool location, status and readiness for production.
Detect issues earlier and prevent major failures.
Prioritize maintenance based on actual need and condition.
Give technicians instant access to history, diagnostics and AI.
Use condition and lifecycle information to improve maintenance decisions.
Critical equipment reliability depended on reactive maintenance, fragmented records and limited visibility into asset condition.
Connected asset monitoring, maintenance execution, condition intelligence and digital work instructions.
Improved coordination, early detection of issues and better production readiness across manufacturing operations.
Connect assets, people, condition data and AI to detect earlier, diagnose faster and keep your critical equipment running.
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