Industrial AI: Applications in Fault Detection, Diagnosis, and Prognosis—2nd Edition
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".
Deadline for manuscript submissions: 30 November 2024 | Viewed by 43
Special Issue Editors
Interests: operation and maintenance engineering
Special Issues, Collections and Topics in MDPI journals
Interests: fault prediction and health monitoring; anomaly detection; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: intelligent fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In the fourth industrial revolution, or Industry 4.0, a key objective is to enhance equipment's ability to perceive its own health state and predict future behavior. The development of artificial intelligence, especially the progress made in deep learning, over the last decade provides a promising tool in bolstering this enhancement. Such a tool can be a complement or alternative to conventional physics-based and signal-processing-based techniques in fault detection, diagnosis, and prognosis applications.
Researchers have started to build data-driven or hybrid models to further boost their prediction accuracy in the above applications, yet there are still some untouched or underexplored territories, such as causal inference, demystifying black-box modelling, domain adaptation, automatic feature learning, etc. This Special Issue aims to present current innovations and engineering achievements of scientists and industrial practitioners in the area of adopting artificial intelligence techniques in fault detection, diagnosis, and prognosis.
Topics of interest include, but are not limited to, the following:
- Adoption of cutting-edge artificial intelligence in prognostics and health management (PHM).
- Data-driven, physics-based, signal-processing-based, or hybrid models straddling the above counterparts.
- Domain adaptation using transfer learning.
- Demystifying the black-box and gaining new insights: interpretability of the learned models.
- Knowledge distillation for edge-computing applications
Dr. Janet Lin
Dr. Liangwei Zhang
Dr. Haidong Shao
Guest Editors
Manuscript Submission Information
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Keywords
- adoption of cutting-edge artificial intelligence in prognostics and health management (PHM)
- data-driven, physics-based, signal-processing-based, or hybrid models straddling the above counterparts
- domain adaptation using transfer learning
- demystifying the black-box and gaining new insights: interpretability of the learned models
- knowledge distillation for edge-computing applications
