Safety Verification and Optimization in Industrial Drive Systems
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913763422109696 |
|---|---|
| author | Hasrat, Imran Riaz Kang, Eun-Young Graulund, Christian Uldal |
| author_facet | Hasrat, Imran Riaz Kang, Eun-Young Graulund, Christian Uldal |
| contents | Safety and reliability are crucial in industrial drive systems, where hazardous failures can have severe consequences. Detecting and mitigating dangerous faults on time is challenging due to the stochastic and unpredictable nature of fault occurrences, which can lead to limited diagnostic efficiency and compromise safety. This paper optimizes the safety and diagnostic performance of a real-world industrial Basic Drive Module(BDM) using Uppaal Stratego. We model the functional safety architecture of the BDM with timed automata and formally verify its key functional and safety requirements through model checking to eliminate unwanted behaviors. Considering the formally verified correct model as a baseline, we leverage the reinforcement learning facility in Uppaal Stratego to optimize the safe failure fraction to the 90 % threshold, improving fault detection ability. The promising results highlight strong potential for broader safety applications in industrial automation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21965 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Safety Verification and Optimization in Industrial Drive Systems Hasrat, Imran Riaz Kang, Eun-Young Graulund, Christian Uldal Software Engineering Safety and reliability are crucial in industrial drive systems, where hazardous failures can have severe consequences. Detecting and mitigating dangerous faults on time is challenging due to the stochastic and unpredictable nature of fault occurrences, which can lead to limited diagnostic efficiency and compromise safety. This paper optimizes the safety and diagnostic performance of a real-world industrial Basic Drive Module(BDM) using Uppaal Stratego. We model the functional safety architecture of the BDM with timed automata and formally verify its key functional and safety requirements through model checking to eliminate unwanted behaviors. Considering the formally verified correct model as a baseline, we leverage the reinforcement learning facility in Uppaal Stratego to optimize the safe failure fraction to the 90 % threshold, improving fault detection ability. The promising results highlight strong potential for broader safety applications in industrial automation. |
| title | Safety Verification and Optimization in Industrial Drive Systems |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2503.21965 |