Modelling the normal behaviour of continuous signals of an industrial plant for anomaly detection and maintenance planning

Efficiency of machines has been an important topic in many areas of industry, at least since the introduction of the term "Industry 4.0". One of the challenges to improve the efficiency of industrial plants is the implementation of anomaly detection systems. These systems are designed to detect faults or wear and tear before a machine failure, for example, and to plan necessary maintenance cycles of machines (or parts) dynamically based on the current status.

A possible approach to solve this challenge by using hybrid time-dependent automata of normal behavior learned from sensor data to model continuous signals. This model reduces continuous signals (e.g. energy data) to single states. This allows an effective anomaly detection of continuous signals to be implemented. Finally, this methodology is compared to classical methods and a preview of other applications, e.g. predictive maintenance planning, is given.