HSU TwinFlow: A Living Benchmark for Evaluating Anomaly Detection and Diagnosis in CPPSs Using Digital-Twin-Generated Data

HSU TwinFlow is a digital-twin-generated dataset for benchmarking anomaly detection and fault diagnosis in cyber-physical production systems. It contains nominal and faulty simulation runs, including training and test scenarios, system measurements, fault labels, and configuration parameters. Code, prior knowledge, baseline methods, and evaluation scripts are available on GitHub: https://github.com/imb-hsu/twinflowThis benchmark is published here: Nemanja Hranisavljevic, Alexander Diedrich, Lukas Moddemann, Domenic Schaeffer, Frank Marek, Ingo Pill, and Oliver Niggemann. “HSU TwinFlow: A Living Benchmark for Evaluating Data-Driven Methods for Anomaly Detection and Diagnosis in Cyber-Physical Production Systems Using Digital-Twin-Generated Data.” In Proceedings of the 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026), Cork, Ireland, 2026.