Artificial Intelligence Explainability (XAI) and Interpretability: Exploring the Potential of XAI in Fault Diagnosis and Cyber-Physical Systems
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".
Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 8072
Special Issue Editors
Interests: autonomous systems; swarm robotics; digital manufacturing; ai in manufacturing; nature-inspired algorithms; human-robot collaboration and industrial automation
Interests: manufacturing informatics, digital manufacturing, industrial Internet of Things, industrial sensor networks, industry 4.0, construction 4.0.
Special Issues, Collections and Topics in MDPI journals
Interests: smart and sustainable manufacturing; life cycle engineering and optimisation; digital product development and manufacturing; cost modelling & engineering economic analysis; circular economy
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligent for condition monitoring; fault diagnosis; prognostic health management; especially for deep learning; transfer learning; few-shot learning method and their application for the large industrial environment; reinforcement learning for control and its applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The application of AI has shown great potential in the past decade. Due to increasing computational resources and data, it is increasingly becoming possible to deploy AI in complex cyber-physical systems such as manufacturing systems, telecommunication systems, IOT-based systems to mention a few. Recent research completed by Deepmind, a leading AI company, has shown how machine learning, especially deep learning techniques, have great potential to reveal and inform how proteins, one of the basic building blocks of life, fold, called AlphaFold: a solution to a 50-year-old grand challenge in biology.
Nevertheless, models produced by current AI techniques, though powerful, are not easily understandable and remain a black box to most practitioners. As a result, it is not known how these models derive their conclusions and what lessons could be learned from their selected decision-making paths to deepen the understanding of the domain. Such an understanding could be important for ethical issues, transparency, and privacy concerns. When applied to fault diagnosis, it could support human engineers in diagnosing faults and providing transparency and explainability of why the faults occurred in cyber-physical systems. This would result in AI systems that can be totally trusted (trustworthy automated systems) and more accepted in manufacturing systems.
This Special Issue invites papers on the rapidly growing field of explainable AI (XAI) theories and methods, as applied to fault diagnosis of systems such as traditional manufacturing systems, smart cyber-physical systems, and remote condition monitoring of equipment to mention a few. We invite you to contribute to this issue by submitting both case studies and research articles, we are open to papers that address (but are not limited to) the following keywords:
- Application of fuzzy logic theory to aid understanding of AI decisions
- New explainable AI (XAI) concepts
- Novel cognitive architectures
- Natural language processing
- Equipment condition monitoring and maintenance
- Human in the loop systems
- XAI and Industry 4.0 / IoT
- Theories, analysis, and visualization of interpretable machine learning/deep learning method
- Industrial applications of interpretable machine learning/deep learning method
- Bayesian networks and probabilistic graphical models
- Knowledge representation and reasoning
- Reasoning under uncertainty
Dr. John Oyekan
Dr. Christopher Turner
Prof. Yuchun Xu
Dr. Ming Zhang
Guest Editors
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