Thinklytics

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.

VP of Operations, Midstream Energy Company

In the client's terms

The question

Is the data in a state that will survive what we want to build on it?

The situation

Maintenance across 340 compressor stations was scheduled on manufacturer intervals alone, with no reference to operating conditions or load. Heavily used stations failed before their service date and lightly used ones were serviced for no reason.

What we found

A fixed interval is a statement about an average station, and there is no average station. The schedule was simultaneously too late for some assets and too early for others, and both errors cost money in different ledgers.

What it produced

Unplanned downtime fell in year one, worth $1.7M annually. Dispatch moved from calendar to condition. Early detection prevented three major compressor failures in the first six months.

The roadmap

Three years of SCADA, vibration and temperature data were analysed alongside maintenance and failure history. A model now scores every station weekly against six specific failure modes, and fixed-interval scheduling was replaced with work orders prioritised by actual risk.

The work behind this result

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Frequently asked questions

Is interval-based maintenance wrong?

It is wrong in both directions at once. Fixed manufacturer intervals across 340 compressor stations here meant heavily used stations failed before their service date while lightly used ones were serviced for no reason. A fixed interval describes an average station and there is no average station.

How do you predict compressor failures?

Model each failure mode separately. Compressor failures present differently depending on mode, each with its own sensor signature and lead time, so six models feed one combined risk score here, scoring all 340 stations weekly.

What does condition-based maintenance save?

$1.7M a year in avoided downtime at this midstream operator, with three major compressor failures prevented in the first six months. Work orders now raise when a station crosses a risk threshold rather than when the calendar says it is due.