Catch Equipment Failures Weeks Early
DOTA AI learns each asset's normal behavior from PI history, flags early deviations, and links them to past outages so repairs are planned, not forced.
Explainer video in production
The interactive walkthrough for Catch Equipment Failures Weeks Early is being produced. The full use case is below.
Illustrative data. No real utility names or plant names are used.
Reliability engineers, maintenance planners, plant managers
Static alarm limits either fire all day long or trigger after the unit is already down, so failures turn into forced outages.
DOTA AI learns each asset's normal behavior from PI history, flags early deviations, and links them to past outages so repairs are planned, not forced.
From a question to a deployed app.
Ask
Learn normal behavior for pumps, fans and transformers from 2 years of PI data and flag early deviations.
Connect
PI historian, CMMS work orders and GADS events.
Learn
DOTA sets an expected band per asset and flags drift, like 23 days outside normal.
Match
It matches the pattern to past trips so maintenance can plan the repair now.
- Unit 2 feedwater pump B: bearing vibration 23 days outside normal
- Same pattern as 2 past trips, 260 forced outage hours
- A planned weekend repair, instead of a peak-season forced outage
- Estimated $640K forced outage cost avoided
The takeaway
Fix it before it fails.
Build catch equipment failures weeks early on your data.
See DOTA AI build a real utility app on your data in a 30-minute working session.