Predicting Downtime Before It Happens: AI Maintenance Across 12 Plants for an Industrial OEM
A real-time predictive maintenance platform that combines IoT telemetry, machine learning, and plant-floor systems to spot equipment failure before it disrupts production.
A global industrial OEM was losing production hours and maintenance budget to unexpected equipment failures across its manufacturing plants. Reactive maintenance meant teams fixed machines only after they broke, while scheduled overhauls often replaced parts that still had useful life. Binariq built a predictive maintenance platform that ingests live sensor data, applies machine-learning models to detect early failure patterns, and integrates with plant-floor systems so maintenance teams can act before downtime occurs.
What made this hard.
- Unplanned downtime on critical assembly lines was costing millions in lost output, expedited parts, and emergency repairs.
- Maintenance relied on fixed schedules and technician intuition, with no data-driven way to prioritize work orders.
- Sensor data existed in plant-floor historians and SCADA systems but was not unified or usable for analytics.
- Different plants used different equipment vintages and protocols, making a one-size-fits-all model impossible.
- There was no reliable early-warning signal to distinguish normal wear from imminent failure.
How we engineered the solution.
Secure IoT platform ingesting vibration, temperature, pressure, and current signals across 12 plants.
Unified pipelines normalizing sensor streams and merging work-order, asset-master, and MES data.
ML models predicting failure windows with explainable risk scores, backed by digital twins.
Alerts and recommendations delivered into CMMS and plant-floor dashboards, with technician feedback loops.
The biggest shift wasn't installing more sensors. It was turning scattered machine data into actionable intelligence and feeding it directly into the systems maintenance teams already use. When predictions are explainable, integrated, and continuously validated on the plant floor, predictive maintenance becomes operational reality rather than a pilot project.
What this engagement proved.
Predictive maintenance succeeds when predictions reach the systems technicians already use, not just dashboards.
Unified data pipelines matter as much as machine-learning models in industrial environments.
Explainable risk scores build trust and drive faster action from maintenance teams.
A continuous feedback loop between predictions and technician outcomes keeps models accurate as conditions change.