Abstract: A system (10) for industrial automation, including a field device (1) with a function unit (2), in particular an actuator unit, sensor unit and/or control unit, which is configured to provide a function in accordance with a received payload (ND), and a communication unit (3) for receiving a payload signal (DS) containing the payload (ND) via a cable (4), the communication unit (3) being configured to provide a parameter set (PS) on the basis of the payload signal (DS) and to carry out signal processing of the payload signal (DS) using the parameter set (PS) in order to obtain the payload (ND) from the payload signal (DS), the system (10) further comprising a diagnosis device (5) which is configured to determine a cable wear condition of the cable (4) in accordance with an indicator variable (IG) based on the parameter set (PS).

Abstract: Continuous condition monitoring of an electrical 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 state model is stored as a one-class model that has been trained in a training phase with normal state data and represents a normal state of the electrical system. Further, the condition monitoring unit comprises a data interface for continuously acquiring sensor data of the electrical 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 (e.g., Euclidean norm, sum norm, maximum norm), a scoring for calculating an anomaly score from the determined deviations, and an output unit for outputting the calculated anomaly score.

Abstract: 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.