Entropy Metrics for Interpretable Condition Monitoring in Complex Industrial Equipment
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: 19 October 2025 | Viewed by 551
Special Issue Editors
Interests: fault diagnosis; machine learning; signal processing
Interests: ocean/underwater wireless sensor networks and path planning
Special Issue Information
Dear Colleagues,
Modern industrial systems demand advanced condition monitoring techniques to ensure reliability and safety. While sensor data availability has increased exponentially, critical challenges remain in extracting physics-interpretable features from complex nonlinear signals—particularly for rotating machinery, power systems, and aerospace equipment. This Special Issue addresses this gap by exploring how entropy-based methods can uniquely bridge physical system modeling and data-driven diagnostics.
We invite cutting-edge research demonstrating entropy metrics' capacity to translate physical degradation mechanisms into actionable data patterns. Specifically, this collection seeks methodologies that establish clear connections between entropy features and specific mechanical failure modes. Submissions should emphasize how proposed techniques overcome current limitations in health indicator construction and fault diagnosis.
Topics of interest include but are not limited to the following:
- Physics-guided entropy algorithms for rotating machinery vibration analysis;
- Multiscale entropy fusion with digital twins for power system fault detection;
- Entropy-driven early warning systems for wind turbine gearbox degradation;
- Hybrid models integrating entropy features with deep learning for aerospace bearing diagnostics;
- Information-theoretic approaches to sensor data fusion in smart manufacturing;
- Validation studies comparing entropy-based health indicators with physical damage measurements.
Dr. Tao Xie
Dr. Xiaojun Mei
Dr. Chaoge Wang
Guest Editors
Manuscript Submission Information
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Keywords
- entropy-based condition monitoring
- interpretable feature extraction
- fault diagnosis
- rotating machinery
- data-driven diagnostics
- predictive maintenance
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