No abstract

No abstract

The importance of safety and reliability in today's real-world complex hybrid systems, such as process plants, led to the development of various anomaly detection and diagnosis techniques. Model-based approaches established themselves among the most successful ones in the field. However, they depend on a model of a system, which usually needs to be derived manually. Manual modeling requires a lot of efforts and resources.This paper gives a procedure for anomaly detection in hybrid systems that uses automatically generated behavior models. The model is learned from logged system's measurements in a hybrid automaton framework. The presented anomaly detection algorithm utilizes the model to predict the system behavior, and to compare it with the observed behavior in an online manner. Alarms are raised whenever a discrepancy is found between these two. The effectiveness of this approach is demonstrated in detecting several types of anomalies in a real-world running production system.

No abstract

In the presented work, the detection of anomalous energy consumption in hybrid industrial production systems is investigated. A model-based approach with a timed hybrid automaton as overall system model is employed for anomaly detection. The approach is based on the assumption of several system modes, i.e. phases with continuous system behavior. Transitions between the modes are attributed to discrete control events such as on/off signals. The underlying discrete event system which comprises both system modes and transitions is modeled as finite state machine. The focus of this paper is set on the modeling of the energy consumption in the particular system modes. Sequences of stochastic state space models are employed for this purpose. Model learning and anomaly detection for this approach are considered. The proposed approach is further evaluated in a small model factory. The experimental results show significant improvements compared to existing approaches to anomaly detection in hybrid industrial systems