Anomaly detection is essential for realizing modern and secure cyber-physical production systems. By detecting anomalies, there is the possibility to recognize, react early, and in the best case, fix the anomaly to prevent the rise or the carryover of a failure throughout the entire manufacture. While current centralized methods demonstrate good detection abilities, they do not consider the limitations of industrial setups. To address all these constraints, in this study, we introduce an unsupervised, decentralized, and real-time process anomaly detection concept for cyber-physical production systems. We employ several 1D convolutional autoencoders in a sliding window approach to achieve adequate prediction performance and fulfill real-time requirements. To increase the flexibility and meet communication interface and processing constraints in typical cyber-physical production systems, we decentralize the execution of the anomaly detection into each separate cyber-physical system. The installation is fully automated, and no expert knowledge is needed to tackle data-driven limitations. The concept is evaluated in a real industrial cyber-physical production system. The test result confirms that the presented concept can be successfully applied to detect anomalies in all separate processes of each cyber-physical system. Therefore, the concept is promising for decentralized anomaly detection in cyber-physical production systems.

In this article, selected new directions in knowledge-based artificial intelligence (AI) and machine learning (ML) are presented: ontology development methodologies and tools, automated engineering of WordNets, innovations in semantic search, and automated machine learning (AutoML). Knowledge-based AI and ML complement each other ideally, as their strengths compensate for the weaknesses of the other discipline. This is demonstrated via selected corporate use cases: anomaly detection, efficient modeling of supply networks, circular economy, and semantic enrichment of technical information.

Effective anomaly detection is essential for realizing modern and secure industrial control systems. However, the direct integration of anomaly detection within such a system is complex due to the wide variety of hardware used, different communication protocols, and given industrial requirements. Many components of an industrial control system allow direct integration, while others are designed as closed systems or do not have the required performance. At the same time, the effective usage of available resources and the sustainable use of energy are more important than ever for modern industry. Therefore, in this paper, we present a modular and hybrid concept that enables the integration of efficient and effective anomaly detection while optimising the use of available resources under consideration of industrial requirements. Because of the modular and hybrid properties, many functionalities can be outsourced to the respective devices, and at the same time, additional hardware can be integrated where required. The resulting flexibility allows the seamless integration of complete anomaly detection into existing and legacy systems without the need for expensive centralised or cloud-based solutions. Through a detailed evaluation within an industrial unit, we demonstrate the performance and versatility of our concept.

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.

This article presents new trends and applications in generative artificial intelligence (AI) and knowledge-based AI. Foundations and applications of large language models (LLMs) are presented, such as classifying hate speech, easy-to-read language, and retrieval-augmented generation (RAG). The important aspect of user experience (UX) for AI systems is elaborated. Finally, AI solutions in different industry sectors are presented, in particular life sciences, the energy sector, and the manufacturing industry.