Fault prediction at pressing shop decreased maintenance costs by 20%
A large manufacturer needed to improve the utilization of pressing lines and predict necessary maintenance windows. Our anomaly-detection-based solution ADF helps a large manufacturer of the automotive industry to predict maintenance windows in pressing lines.
The platform can detect outliers, anomalies, and unwanted behavior in large and complex data. ADF analyses data from sensors on production lines, looking for early warnings leading to a potential malfunction.
Therefore, we used data from sensors in order to create a predictive model detecting the need for pro-active maintenance during scheduled down times, we decreased idle time on pressing lines and maintenance costs by at least 20%.