Twin‐engined diagnosis of discrete‐event systems
Diagnosis of discrete‐event systems (DESs) is computationally complex. This is why a variety of knowledge compilation techniques have been proposed, the most notable of them rely on a diagnoser. However, the construction of a diagnoser requires the generation of the whole system space, thereby makin...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-11-01
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| Series: | Engineering Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/eng2.12060 |
| _version_ | 1818359170897805312 |
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| author | Nicola Bertoglio Gianfranco Lamperti Marina Zanella Xiangfu Zhao |
| author_facet | Nicola Bertoglio Gianfranco Lamperti Marina Zanella Xiangfu Zhao |
| author_sort | Nicola Bertoglio |
| collection | DOAJ |
| description | Diagnosis of discrete‐event systems (DESs) is computationally complex. This is why a variety of knowledge compilation techniques have been proposed, the most notable of them rely on a diagnoser. However, the construction of a diagnoser requires the generation of the whole system space, thereby making the approach impractical even for DESs of moderate size. To avoid total knowledge compilation while preserving efficiency, a twin‐engined diagnosis technique is proposed in this paper, which is inspired by the two operational modes of the human mind. If the symptom of the DES is part of the knowledge or experience of the diagnosis engine, then Engine 1 allows for efficient diagnosis. If, instead, the symptom is unknown, then Engine 2 comes into play, which is far less efficient than Engine 1. Still, the experience acquired by Engine 2 is then integrated into the symptom dictionary of the DES. This way, if the same diagnosis problem arises anew, then it will be solved by Engine 1 in linear time. The symptom dictionary can also be extended by specialized knowledge coming from scenarios, which are the most critical/probable behavioral patterns of the DES, which need to be diagnosed quickly. |
| first_indexed | 2024-12-13T20:40:39Z |
| format | Article |
| id | doaj.art-da0beacfc38e4ff7ba5f9c5816d3c98f |
| institution | Directory Open Access Journal |
| issn | 2577-8196 |
| language | English |
| last_indexed | 2024-12-13T20:40:39Z |
| publishDate | 2019-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj.art-da0beacfc38e4ff7ba5f9c5816d3c98f2022-12-21T23:32:09ZengWileyEngineering Reports2577-81962019-11-0114n/an/a10.1002/eng2.12060Twin‐engined diagnosis of discrete‐event systemsNicola Bertoglio0Gianfranco Lamperti1Marina Zanella2Xiangfu Zhao3Department of Information Engineering University of Brescia Brescia ItalyDepartment of Information Engineering University of Brescia Brescia ItalyDepartment of Information Engineering University of Brescia Brescia ItalyCollege of Mathematics, Physics and Information Engineering Zhejiang Normal University Jinhua ChinaDiagnosis of discrete‐event systems (DESs) is computationally complex. This is why a variety of knowledge compilation techniques have been proposed, the most notable of them rely on a diagnoser. However, the construction of a diagnoser requires the generation of the whole system space, thereby making the approach impractical even for DESs of moderate size. To avoid total knowledge compilation while preserving efficiency, a twin‐engined diagnosis technique is proposed in this paper, which is inspired by the two operational modes of the human mind. If the symptom of the DES is part of the knowledge or experience of the diagnosis engine, then Engine 1 allows for efficient diagnosis. If, instead, the symptom is unknown, then Engine 2 comes into play, which is far less efficient than Engine 1. Still, the experience acquired by Engine 2 is then integrated into the symptom dictionary of the DES. This way, if the same diagnosis problem arises anew, then it will be solved by Engine 1 in linear time. The symptom dictionary can also be extended by specialized knowledge coming from scenarios, which are the most critical/probable behavioral patterns of the DES, which need to be diagnosed quickly.https://doi.org/10.1002/eng2.12060communicating automatadiscrete‐event systemsknowledge compilationmodel‐based diagnosis |
| spellingShingle | Nicola Bertoglio Gianfranco Lamperti Marina Zanella Xiangfu Zhao Twin‐engined diagnosis of discrete‐event systems Engineering Reports communicating automata discrete‐event systems knowledge compilation model‐based diagnosis |
| title | Twin‐engined diagnosis of discrete‐event systems |
| title_full | Twin‐engined diagnosis of discrete‐event systems |
| title_fullStr | Twin‐engined diagnosis of discrete‐event systems |
| title_full_unstemmed | Twin‐engined diagnosis of discrete‐event systems |
| title_short | Twin‐engined diagnosis of discrete‐event systems |
| title_sort | twin engined diagnosis of discrete event systems |
| topic | communicating automata discrete‐event systems knowledge compilation model‐based diagnosis |
| url | https://doi.org/10.1002/eng2.12060 |
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