"Machine Learning" is a subfield of Artificial Intelligence (AI) that uses statistical models based on training data to make certain statements. Thus, patterns and regularities in the learning data can be recognized and analyzed with unknown test data. With machine learning, for example, anomalies can be detected, states can be classified, but also a behavior in the future can be predicted. Of course, always with a calculated probability based on the experience and data from what has been learned.
In the area of sensor technology, Fraunhofer EMFT uses machine learning to process the collected sensor data, which is usually available as time series. This involves using "unsupervised" approaches for anomaly detection as well as "supervised" approaches for classification and pattern recognition for assignment to a specific state. In production engineering, for example, sensor systems with machine learning are used for "predictive maintenance". This predictive maintenance makes it possible to predict necessary plant maintenance before a failure is imminent. This allows expensive, regular maintenance work to be reduced to only what is needed.