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PREDICTIVE MAINTENANCE

Understand equipment
health in operational
context

Equipment performance isn't captured in one sensor or one system.

DeepIQ connects historian data, maintenance history, operating conditions, asset information,
engineering context, and expert knowledge so teams can understand equipment health and identify problems earlier.

Maintenance data is everywhere. Equipment context shouldn't be.

Understanding why equipment is behaving differently often requires information from multiple systems.

Sensor readings live in historians. Work orders live in maintenance systems. Asset information lives somewhere else. Operating conditions provide another part of the story.

DeepIQ connects these sources around the equipment itself so maintenance and reliability teams can evaluate performance with the context surrounding it.

Move from isolated signals to connected equipment intelligence

Build a unified asset view
Connect operational, maintenance, engineering, and enterprise information around each asset.
Understand operating context
Evaluate equipment behavior relative to operating conditions instead of looking at sensor values in isolation.
Identify abnormal behavior
Detect patterns and conditions that may indicate developing equipment problems.
Use expert knowledge
Combine historical data with engineering and maintenance knowledge when failure examples are limited.
Prioritize maintenance
Help teams focus attention on equipment and conditions that require investigation.
Preserve explainability
Connect alerts and predictions back to the information and operational context behind them.

CUSTOMER STORIES

“DeepIQ was just what we needed to successfully meet the scope and schedule requirements of our integration.”
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Jimmy HolmesDirector Data Management & BI

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