Using machine learning for the early detection of anomalies helps to avoid damages
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. In addition, there is usually only little information available on anomalous events. As a result, it is difficult for the system to identify and categorize exceptional states. However, this knowledge is important in order to find out how precarious the respective deviations from the norm really are. These problems are to be addressed in the project »Machine Learning Procedures for Stochastic-Deterministic Multi-Sensor Signals« (MADESI).