Predicting which asset fails next
industrial energy operator · Gulf
The shape of this problem
The same judgment, made differently every time
This is one of our own builds, not a client engagement. It is capability evidence and it is described as such.
The problem
Energy operators needed to reduce unplanned equipment failures, unnecessary scheduled maintenance, and the cost of making maintenance decisions without timely condition insight. Calendar-based maintenance is wrong in both directions simultaneously - it services healthy equipment and misses deteriorating equipment - but the usual alternative fails on alert fatigue, because startup transients, shutdowns, and faulty instrumentation generate the same warning as a genuine emerging fault.
What we built
We built a pipeline that collected IoT readings and equipment logs, calculated health indicators, detected abnormal patterns, predicted failure risk, and generated early warnings with recommended maintenance actions.
What moved
| Measure | Before | After |
|---|---|---|
| Unplanned downtime | † | down 30% |
| Maintenance expenditure | † | down 20% |
| Equipment availability | † | up 15% |
† Measured against the prior level of the same measure. The source publishes the size of the movement, not the figure it moved from.
Unplanned downtime
Measured against the client's own prior process, indexed to 100. The source publishes the size of the movement, not the absolute figure it moved from.
Maintenance expenditure
Measured against the client's own prior process, indexed to 100. The source publishes the size of the movement, not the absolute figure it moved from.
Equipment availability
Measured against the client's own prior process, indexed to 100. The source publishes the size of the movement, not the absolute figure it moved from.
Figures are drawn from the practice's own delivery records for the engagement named, measured against the process that preceded it. They have not been through third-party audit, and none is presented as an average across clients.
What it turned on
Separating operating regimes and sensor faults from genuine equipment anomalies is what made the signal actionable - startup, shutdown, and bad instrumentation stopped producing failure warnings, which is the specific reason technicians could trust an alert enough to schedule work against it.
- Service line
- Forecasting & Planning AI
The finding matters more than the number here. Both cases repaired data definitions before modelling, which is the practice's whole thesis in miniature.
Start here
Which of the four is yours?
Tell us the documents and the monthly volume and we will send the two closest records, with the proof behind each and the honest note where the match is partial.
You get a reply within one working day, from the engineer who would do the work - not a sales sequence.