Digital Twin-Enabled Deep Learning for Machinery Health Monitoring
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 10 March 2025 | Viewed by 83
Special Issue Editors
Interests: fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Interests: condition monitoring and fault diagnosis; gearbox dynamics and diagnostics; gear tribology; signal processing
Special Issues, Collections and Topics in MDPI journals
Interests: fault diagnosis; mechanical engineering; signal processing; eature extraction; condition monitoring
Special Issues, Collections and Topics in MDPI journals
Interests: fault diagnosis; mechanical engineering; deep learning; feature extraction; condition monitoring
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Digital twin technology, which creates high-fidelity virtual replicas of physical assets, is transforming numerous industries by enabling real-time monitoring, diagnostics, and optimization. When coupled with deep learning, a subset of artificial intelligence adept at handling large datasets and uncovering intricate patterns, digital twins can significantly enhance machinery health monitoring. This integration allows for more accurate fault detection, predictive maintenance, and overall system reliability, ultimately reducing downtime and maintenance costs.
This Special Issue will present the latest research findings, technological advancements, and practical applications related to integrating digital twins with deep learning for machinery health monitoring. This Special Issue encourages submissions that cover, among others, the following topics:
- Advancements in digital twin for machine fault diagnosis;
- Integration of digital twins and deep learning algorithms for health monitoring;
- Advanced sensing and monitoring techniques under variable working conditions;
- Transfer-learning-based mechanical fault diagnosis and prognosis.
Dr. Ke Zhao
Dr. **ngkai Yang
Dr. Zongzhen Zhang
Prof. Dr. **rui Wang
Guest Editors
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- digital twin for machine fault diagnosis
- digital twin for health monitoring
- advanced sensing and monitoring techniques
