The HAI-CPPS Benchmark: Evaluating AI Capabilities Across Hybrid Data Spaces
Abstract
A long-term objective for many research fields, such as anomaly detection, discretization and root-cause diagnosis in Cyber-Phyiscal Production Systems is the realization of resilient and highly autonomous systems. Instances of those systems range from the control and operation of single production systems to controlling entire plants. While notable progress has been made, a key challenge remains unaddressed: the availability of comprehensive and standardized datasets necessary for advancing machine learning based solutions. Typical evaluation datasets comprise systems of (too) little complexity or are either suitable for only data-driven or only for symbolic methods. Yet, a comprehensive dataset and model for developing, training, and testing machine learning methods from anomaly detection to fault diagnosis in a structured and comparable manner does not exist. To bridge this gap, we present a benchmark specifically designed to support both data-driven methods and symbolic reasoning approaches, by extending the BerFiPl benchmark introduced by Ehrhardt et al.. By providing hybrid data streams, labeled system states, and a modular, interpretable system structure, the dataset offers a unique opportunity to develop, train, test, and compare hybrid AI methods to bridge the gap between data-driven and symbolic paradigms.
Citation: L. Moddemann, J. Ehrhardt, A.Diedrich, O. Niggemann, “The HAI-CPPS Benchmark: Evaluating AI Capabilities Across Hybrid Data Spaces,” ETFA - IEEE Conference on Emerging Technologies and Factory Automation, 2025. doi:http://dx.doi.org/10.1109/ETFA65518.2025.11205680.