Machine learning method for leakage detection in a pneumatic system

    公开(公告)号:US20230088241A1

    公开(公告)日:2023-03-23

    申请号:US17932773

    申请日:2022-09-16

    申请人: Festo SE & Co. KG

    摘要: Continuous condition monitoring of a pneumatic system, and in particular for early fault detection, is provided. The condition monitoring unit is formed with an interface to a memory in which a trained normal condition model is stored as a one-class model, which has been trained in a training phase with normal condition data and represents a normal condition of the pneumatic system. Furthermore, the condition monitoring unit comprises a data interface for continuously acquiring sensor data of the pneumatic system by means of a set of sensors, an extractor for extracting features from the acquired sensor data, a differentiator for determining deviations of the extracted features from learned features of the normal state model by means of a distance metric, a scoring unit for calculating an anomaly score from the determined deviations, and an output unit for outputting the calculated anomaly score.