Abstract
Enhancing systems with advanced diagnostic capabilities for detecting,
locating, and compensating faults during operation increases autonomy and
reliability. To assure that the diagnosis-enhanced system really has improved
reliability, we need – besides other means – to check the correctness of the
diagnosis functionality. In this paper, we contribute to this challenge and
discuss the application of testing to the case of model-based diagnosis, where
we focus on testing the system models used for fault detection and local-
ization. We present a simple use case and provide a step-by-step discussion
on introducing testing, its capabilities, and arising issues. We come up with
several challenges that we should tackle in future research.
locating, and compensating faults during operation increases autonomy and
reliability. To assure that the diagnosis-enhanced system really has improved
reliability, we need – besides other means – to check the correctness of the
diagnosis functionality. In this paper, we contribute to this challenge and
discuss the application of testing to the case of model-based diagnosis, where
we focus on testing the system models used for fault detection and local-
ization. We present a simple use case and provide a step-by-step discussion
on introducing testing, its capabilities, and arising issues. We come up with
several challenges that we should tackle in future research.
| Originalsprache | englisch |
|---|---|
| Titel | Industrial Artificial Intelligence Technologies and Applications |
| Redakteure/-innen | Ovidiu Vermesan, Franz Wotawa, Mario Diaz Nava, Björn Devaille |
| Erscheinungsort | Gistrup |
| Herausgeber (Verlag) | River Publishers |
| Kapitel | 14 |
| Seiten | 189-203 |
| ISBN (elektronisch) | 978-87-7022-790-2 |
| ISBN (Print) | 978-87-7022-791-9 |
| Publikationsstatus | Veröffentlicht - 2022 |
Publikationsreihe
| Name | River Publishers Series in Communications |
|---|
Fields of Expertise
- Information, Communication & Computing