A European manufacturer operating 12 production lines was haemorrhaging millions to unplanned downtime. We deployed IoT sensor integration with ML-based anomaly detection that predicts equipment failures 72 hours in advance, transforming a reactive maintenance culture into a proactive, data-driven operation.
The client, a mid-market European manufacturer with 12 active production lines, was trapped in a cycle of reactive maintenance. Equipment failures were unpredictable, disruptive, and devastatingly expensive.
“We had mountains of sensor data sitting in databases no one ever looked at. Every time a machine went down, the entire line stopped and we scrambled. It was firefighting, not engineering. We knew there had to be a better way, but we did not have the in-house expertise to build predictive systems.”
— VP of Manufacturing OperationsThe organisation had invested heavily in IoT infrastructure during a previous digitalisation initiative, but lacked the data science capability to extract actionable intelligence from the sensor streams. Maintenance teams relied on scheduled inspections and experience-based intuition, missing early warning signs buried in vibration, temperature, and power consumption data.
We began with a comprehensive audit of all 2,400+ IoT sensors across the 12 production lines. Our team catalogued every data source, assessed signal quality and sampling rates, identified gaps in coverage, and mapped which sensors correlated with historically documented failure modes. This gave us a clear picture of the data landscape and highlighted the key failure indicators we would build our models around.
Using 18 months of historical sensor data paired with maintenance logs, we trained an ensemble of ML models specialising in different failure signatures. The models analysed vibration frequency spectra, temperature drift patterns, power consumption anomalies, and pressure fluctuations. Ensemble methods combining isolation forests, autoencoders, and gradient-boosted classifiers gave us robust detection across diverse failure types.
We built a real-time monitoring dashboard that visualises equipment health scores across every production line. The system delivers 72-hour failure predictions with confidence levels, integrates directly with the existing maintenance scheduling system via API, and provides tiered escalation alerts to the right personnel based on severity and urgency. Shift supervisors, maintenance leads, and plant managers each see views tailored to their decision-making needs.
Every prediction outcome feeds back into the system. When maintenance teams act on an alert, they record what they found, whether the prediction was accurate, and what action was taken. This continuous feedback loop powers automated model retraining on a weekly cadence, steadily improving prediction accuracy. Over the first three months, false positive rates dropped by 40% as the models learned from real operational outcomes.
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