Predictive Maintenance
Prioritized by production impact, not by failure probability alone.
Predictive maintenance projects usually fail for one of two reasons: the model predicts failures nobody can act on within the warning window, or it predicts them on equipment whose failure does not actually matter. Both are scoping errors rather than modeling errors, which is why the first phase of this work is about the maintenance operation, not the algorithm.
Book a free consultationWhy this is usually broken
- Warning windows shorter than the parts lead time are informative but not actionable
- Failure probability ranked without production impact sends crews to the wrong assets
- Maintenance history is often free text in a CMMS and unusable without processing
- Alarm fatigue kills adoption faster than poor accuracy does
How we build it
Scope to the action, not the prediction
We start from what maintenance can act on and how long it takes, then build to that window. A prediction outside the actionable window is not a product.
Impact-weighted ranking
Alerts ranked by deferred production and downstream effect, not by model confidence.
Work order history as a signal
Free-text maintenance history is one of the strongest available signals and is almost always ignored because it needs language processing to use.
Tuned against alarm fatigue
Thresholds set with the crew, revisited after the first weeks. A precise model nobody trusts is worth nothing.
What changes
Figures are drawn from Senteras engagements and are illustrative of typical results. Outcomes vary by data quality, infrastructure and scope.
Common questions
How much historical data do we need?
Enough failures to learn from, which is usually the binding constraint rather than sensor coverage. On assets that rarely fail, anomaly detection against normal operation is the more honest approach than failure prediction.
Where we deploy this
Manufacturing & Supply Chain
Reduce downtime, improve quality, and build a more resilient supply chain with AI.
Energy & Oil and Gas
Subsurface, reliability and land data intelligence, without sending proprietary data offsite.
Logistics & Supply Chain
Forecasting, exception handling and trade documents on your own data.
The services behind it
Model Fine-Tuning & Integration
Models that speak your industry's language, trained on your data.
Custom AI Agents & Automation
AI that doesn't just answer questions. It gets things done.
AI Strategy & Roadmap
A clear path from where you are to where AI can take you.
Start with a conversation, not a proposal
Thirty minutes. We will tell you what we would change first, and whether you need us at all.
Book a callThe firm behind the firm