An AI benchmark for diagnosis, reconfiguration & planning
Publication date
2022-10-25
Document type
Conference paper
Author
Organisational unit
Conference
27th International Conference on Emerging Technologies and Factory Automation (ETFA 2022) ; Stuttgart, Germany ; September 6–9, 2022
Publisher
IEEE
Book title
2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Keyword
Fault diagnosis
Cyber-Physical Production System
Benchmark testing
AI planning
Reconfiguration
Anomaly detection
Abstract
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. Within this paper we identify minimum common set requirements for AI benchmarks in the domain of CPPS and 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. We evaluate the benchmark on state-of-the-art AI approaches in diagnosis, reconfiguration, and planning. The benchmark is made publicly available on GitHub.
Version
Published version
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