Work

Process Anomaly Detection in Cyber–Physical Production Systems Based on Conditional Discrete-Time Dynamic Graphs

Year: 2025

Type: article

Abstract: Various types of anomalies can arise in cyber–physical production systems, caused by either faulty devices or incorrect processes. Anomalies within individual devices can often be detected by applying machine learning techniques to the respective produced multivariate time series. While this data typically shows temporal and spatial changes and can therefore be efficiently utilized by models, detecting anomalies within the process is often more challenging, as process data usually only consists of events, binary signals, or changes in unique process states. Due to the low variance of data, existing anomaly detection methods struggle to detect anomalies effectively and accurately. To address this challenge, in this paper, we propose a novel concept for process anomaly detection based on conditional discrete-time dynamic graphs. Through the conditional connections of the graph, essential characteristics can be generated and utilized to effectively train machine learning models to detect anomalies in the process data. Identified anomalies can be related to the current graph, facilitating transparent and explainable detections. By evaluating the concept against process data from an industrial unit and achieving an F1-Score of 0.96 and 1 for the realized repetitive processes, the accuracy and effectiveness of the concept can be demonstrated.

Source: Applied Sciences

DOI: 10.3390/app152111354

Authors: Christian Goetz, Bernhard G. Humm

Institution: Darmstadt University of Applied Sciences

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896