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Case Study

Optimize a manufacturing process with anomaly detection

Train predictive models on manufacturing process time-series data to predict and prevent future faults


Our client is an international glass manufacturer that needed to improve product quality in their manufacturing process. Our client was experiencing faults in their product, caused by inconsistencies in the manufacturing process. The client needed to understand where in the process the faults were being introduced to enable intervention and prevention.


Our team aggregated time series data for 250 sensors in the manufacturing process including flow rates, temperature, and energy expenditure. Feature engineering focused on aggregating and summarizing sensor data in various time increments. Machine learning model experimentation included: linear models, advanced regression, clustered feature reduction, neural net, sliding window averaging, automated peak detection, time series clustering. We identified the most predictive models enabling the client to significantly reduce product faults.

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