Developing a predictive model.
The objective was to predict process conditions that could lead to production stops or failures during a sterilization process. As the project evolved, the scope was expanded to include the prediction of process alarms before they occurred.
The sterilization cycle lasted 45 minutes, meaning that any failure resulted in considerable production losses and delayed operator response..
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Using advanced data analysis and process modelling, we developed a predictive model capable of identifying process deviations at a very early stage. Rather than focusing on AI as a starting point, we first ensured that the available production data was reliable, structured, and suitable for accurate modelling.
Challenge
After an earlier project had shown that poor data quality can significantly limit the value of advanced analytics, the customer wanted to explore the potential of predictive process monitoring on a second production line where the available data was suitable for modelling.
Customer
Our customer is a leading pharmaceutical manufacturer operating highly automated production lines where process stability, product quality, and operational reliability are critical.
The solution: Predictive model capable of identifying process deviations at a very early stage.
By continuously monitoring process behavior in real time and detecting early signs of anomalies, the solution provides operators with actionable insights into potential future issues, enabling them to take preventive actions before deviations impact production.
Continuous data quality monitoring
Sensor data quality is continuously monitored to ensure reliable predictions and maintain confidence in the generated insights.
Predictive alarm monitoring
The solution was extended to predict process alarms before they occurred, giving operators valuable time to take corrective action.
Early failure prediction
The developed model predicts, with a high degree of confidence, within the first five minutes whether a sterilization cycle is likely to fail, despite the full process taking 45 minutes.
Process data modelling
Historical production data was analysed to identify patterns that consistently preceded process failures and alarms..
Industrial data assessment
We evaluated the quality and consistency of the available process data to ensure it was suitable for predictive modelling.
Customer value.
A scalable data foundation for future digitalization and AI initiatives.
By identifying potential failures within the first five minutes of a 45-minute sterilization cycle, the customer can prevent unnecessary production losses and intervene before issues escalate. Operators gain valuable time to take corrective action, reducing downtime and improving overall process stability.
By building a strong data foundation, the customer is now able to leverage predictive analytics with confidence, creating immediate operational value while laying the groundwork for future AI-driven innovations.
Meet the expert.
Nele Perdu
Team lead Industrial IT
Co-funded by
EUROSTARS reference number : E!114650
