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Abstract
Research on cyber-physical systems comes to the fore with the increasing progress of applications in the field of autonomous systems. Therefore, there is a growing interest in methods for enhancing reliability, availability, and self-adaptation of such systems in safety critical situations. Hence, it is essential that autonomous systems are equipped with a detection system to observe faulty behaviour in real time or to predict failing operations to avoid safety critical scenarios, which may harm people. To bring or hold a system within healthy conditions, not only detecting a faulty behaviour is important, but also to find the corresponding root cause.
In this article, we introduce different methods which make use of detecting unexpected behaviour in cyber-physical systems, for the localization of faults. The first approach, model-based diagnosis uses logic to represent a cyber-physical system to perform reasoning for computing diagnosis candidates. A second promising approach deals with simulation- based diagnosis systems, using digital twin models to produce faulty behaviour data in advance, and to find correlations with the original cyber- physical system’s behaviour, for diagnosis. For the third method the focus is set on artificial intelligence (machine learning and neural networks), where the goal is to utilize a huge amount of health and safety critical observations of the system for training to approximate the behaviour associated with faulty and safety critical states.
In this article, we introduce different methods which make use of detecting unexpected behaviour in cyber-physical systems, for the localization of faults. The first approach, model-based diagnosis uses logic to represent a cyber-physical system to perform reasoning for computing diagnosis candidates. A second promising approach deals with simulation- based diagnosis systems, using digital twin models to produce faulty behaviour data in advance, and to find correlations with the original cyber- physical system’s behaviour, for diagnosis. For the third method the focus is set on artificial intelligence (machine learning and neural networks), where the goal is to utilize a huge amount of health and safety critical observations of the system for training to approximate the behaviour associated with faulty and safety critical states.
Original language | English |
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Title of host publication | Artificial Intelligence for Digitising Industry |
Editors | Ovidiu Vermessen, Reiner John, Cristina De Luca, Marcello Coppola |
Publisher | River Publishers |
Chapter | 1.4 |
Pages | 47-61 |
ISBN (Electronic) | 9788770226639 |
ISBN (Print) | 9788770226646 |
DOIs | |
Publication status | Published - Sept 2021 |
Keywords
- Artificial Intelligence (AI)
- Industrial Internet of Things (IIoT)
- Machine learning
- Deep learning
- Neural networks
- Machine vision
- Smart robots
- AI at the edge
- Silicon-born AI Industrial sectors
- automotive
- Semiconductor
- industrial machinery
- food and beverage
- transportation
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Dive into the research topics of 'Foundations of Real Time Predictive Maintenance with Root Cause Analysis'. Together they form a unique fingerprint.Projects
- 1 Finished
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AI4DI - Artificial Intelligence for Digitising Industry
Wotawa, F. (Co-Investigator (CoI))
1/05/19 → 31/12/22
Project: Research project