Machine downtime is a key cost element for industrial production.
Even very basic machines expose data from more than 50 sensors and with more than 150 dimensions per data point. Often this data is not used for optimizing utilization and reduction of expensive downtime. AGT is working with key industrial partners to reduce machine downtime through deployment of advanced analytics for machine state monitoring, anomaly detection, and failure prediction.
IoTA’s analytics pipeline enables detection of operational modes from raw sensor data, identification of machine activities, automatic analysis of abnormal behavior and prediction of wear/potential downtime.
AGT’s solutions not only were able to predict specific machine failure up to multiple days earlier than industrial benchmarks, but also allowed to identify and investigate misconfiguration and potential for improved machine utilization.