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 by 41%, 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.
Outcome
We cut unplanned downtime by 41% in the first year, saving $1.7 million annually. We improved maintenance labor efficiency by 23% by shifting from fixed schedules to condition-based dispatch. Early detection prevented three major compressor failures within six months.
Results
- 41% Reduction in unplanned downtime
- $1.7M Annual downtime cost savings
- 23% Maintenance labor efficiency improvement
- 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.