Anomaly Detection for your emission systems.

Regular Anomaly Detection methods are not making a significant impact in the production line when it comes to predicting equipment failure. Reduction in Operating costs have been statistically insignificant due to the lower accuracy levels of these models resulting in poor equipment maintenance. Therefore, Predictive maintenance allows manufacturers to lower maintenance costs, extend equipment life, reduce downtime and improve production quality by addressing problems before they cause equipment failures.

Traditional method

Accuracy 45-65%

48 Weeks


Accuracy 80-90%

4 Weeks

At Mate Labs, Using our AI-powered platform "MateVerse", we can detect anomalies in your emission systems to help improve conversion efficiency with an accuracy level of 98% in all under flat three weeks by looking into factors such as

1  Mass Flow Rate

2  Temperature

3  Catalyst Efficiency

4  Dosing Quantity

5  Upstream emission value

Anomaly detection is about classifying outliers and using historical data. It uses multiple variables collected from various IoT sensors and automatically relating it. With the notable potential to enhance predictive maintenance and also detecting relevant features to give a better conversion efficiency of the emission system.