Using machine learning for the early detection of anomalies helps to avoid damage

The analysis of sensor data of machines, plants or buildings makes it possible to detect anomalous states early and thus to avoid further damage. For this purpose, the monitoring data is searched for anomalies. By means of machine learning, anomaly detection can already be partially automated. Machine learning methods first require a stable learning phase in which they get to know all possible kinds of regular states. For wind turbines or bridges, this is only possible to a very limited extent, as they are, for example, exposed to highly fluctuating weather conditions.