Midstream Energy Company · Energy & Utilities · Houston, TX · 16 weeks
Predictive maintenance AI deployed across 340 compressor stations
A midstream energy company with 340 natural gas compressor stations faced $4.2M yearly losses from unplanned downtime. Their maintenance followed fixed schedules without considering equipment health. We implemented a predictive maintenance AI that evaluated each station weekly. This cut unplanned downtime, saving the company $1.7M annually.
Challenge
The company scheduled maintenance solely on manufacturer guidelines, ignoring real operating conditions and usage patterns. As a result, heavily used stations broke down before their service dates, while lightly used ones received unnecessary maintenance. We analyzed equipment usage and adjusted schedules based on actual load and environment data.
Approach
We analyzed three years of sensor data from SCADA, vibration, and temperature systems alongside maintenance and failure records. Using this data, we created a model that scores each station weekly on the likelihood of six specific failure types. This allowed us to replace the fixed-interval maintenance schedule with prioritized work orders based on actual risk.
Outcome
We cut unplanned downtime in the first year, saving $1.7 million annually. We shifted from fixed schedules to condition-based dispatch. Early detection prevented three major compressor failures within six months.
How We Created Scores to Catch All the Ways Things Could Break Down
Compressor failures don’t all look the same, they show up in six different ways, each with its own set of sensor signals and warning times. So, we built a separate predictive model for each failure type. Then, we combined those results into one risk score. That way, the maintenance team got a simple heads-up on what was about to break and exactly how much time they had to fix it.
How We Dug Into Work Orders to Find Workflow Holes and Data Slip-Ups
We plugged the predictive scores right into the work order system. So now, maintenance only happens when a station’s risk hits a certain point. No more random, routine checks just because the calendar says so. We’re fixing things based on actual risk. Way smarter.
Results
- $1.7M Downtime savings, year one
- $1.7M Annual downtime cost savings
- Weekly Failure-risk scoring across all 340 stations
- 3 Major compressor failures prevented in first 6 months
We used to spend $4.2 million a year on unplanned downtime because our maintenance was based on a calendar, not on how the equipment was actually doing. Thinklytics built a model that predicts which stations might fail. In the first six months, we cut unplanned downtime by 41% and avoided three major failures.