Data sets from CPS for ML applications
To improve the autonomy of Cyber-Physical Production Systems (CPPS), a growing number of approaches in Artificial Intelligence (AI) is developed. However, implementations of such approaches are often validated on individual use-cases, offering little to no comparability. Though CPPS automation includes a variety of problem domains, existing benchmarks usually focus on single or partial problems. Additionally, they often neglect to test for AI-specific performance indicators, like asymptotic complexity scenarios or runtimes. We introduce a comprehensive benchmark, offering applicability on diagnosis, reconfiguration, and planning approaches from AI. The benchmark consists of a grid of datasets derived from 16 simulations of modular CPPS from process engineering, featuring multiple functionalities, complexities, and individual and superposed faults.
AI Benchmark for Diagnosis Reconfiguration and Planning
| Title | Authors | Year |
|---|---|---|
| An AI benchmark for Diagnosis, Reconfiguration & Planning | Erhardt et al. | 2022 |
jonas.ehrhardt{@}hsu-hh.de