Evaluating Deep Learning Models for Anomaly Detection in an Industrial Transporting System
Cyber Physical Production Systems (CPPS) require robust techniques for detecting anomalies and root causes in the system. The model based diagnosis is a commonly used approach in which a dynamic process model captures spatio temporal features of the system's behavior. Because of the infeasibility of precise mathematical or expert modeling, algorithms have been developed for learning such models from system observations. These algorithms are characterized by high domain specialization and yield relatively poor performance in other use cases. In this paper the CPPS data is used, on which existing models have proven ineffective. The perspective of applying deep learning approach to constructing a process model in such systems is investigated. The main idea is to go from models with fixed structure to more universal techniques for learning optimal structure from dynamic observations. The challenges of evaluating dynamic system models of this class are identified, and evaluation criteria are proposed for representative comparison and benchmarking of the models. It is shown that deep learning models provide increase in anomaly detection score but require additional verification of model robustness.
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