Multivariate Entropy-Informed Fault Diagnosis and Structural Health Monitoring
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".
Deadline for manuscript submissions: 31 May 2026 | Viewed by 23
Special Issue Editors
Interests: multivariate signal processing; nonlinear dynamics; mechanical fault diagnosis; RUL prediction; structural health monitoring
Special Issues, Collections and Topics in MDPI journals
Interests: smart manufacturing; 3D printing; augmented reality; brain–computer interface; energy manufacturing and production scheduling optimization
Special Issues, Collections and Topics in MDPI journals
Interests: digital twin; artificial intelligence; intelligent operation and health management; mechanical friction dynamics; life prediction and health evaluation methods
Special Issues, Collections and Topics in MDPI journals
Interests: power system and electrical equipment testing, diagnosis, forecasting and health management; intelligent sensing and information processing; data fusion and artificial intelligence; service quality evaluation of new energy equipment
Special Issue Information
Dear Colleagues,
Modern engineering systems—such as rotating machinery, power transmission components, energy storage devices, and large-scale civil or aerospace structures—operate under dynamic and uncertain conditions. Variations in load, speed, and environment generate nonstationary and multichannel signals whose statistical properties evolve over time, posing persistent challenges for reliable fault diagnosis and structural health monitoring. Traditional scalar indicators or single-sensor features are often distorted by noise, transient fluctuations, and cross-coupled fault mechanisms, resulting in reduced accuracy and interpretability.
Entropy-based complexity analysis provides a powerful information-theoretic tool to quantify irregularity, uncertainty, and dependence within measured signals. Extending conventional univariate formulations, multivariate entropy constructs joint embedding vectors across multiple sensors to reveal spatial correlations, phase relationships, and lagged dependencies. By quantifying joint uncertainty rather than independent variability, MvE captures intrinsic cross-channel dynamics while mitigating the effects of redundant or condition-induced variations. These advantages make it particularly suited for analyzing heterogeneous, multi-sensor data from complex engineering systems.
When combined with modern learning and fusion frameworks, multivariate entropy-based indicators enable interpretable and physics-guided fault diagnosis. They provide compact yet expressive representations that enhance classifier generalization, improve sensitivity to incipient degradations, and reduce dependence on extensive labeled datasets.
Ultimately, multivariate entropy-informed fault diagnosis and SHM frameworks offer a unified paradigm for integrating heterogeneous sensing data, improving diagnostic robustness, and enabling intelligent, condition-aware monitoring of mechanical and structural systems throughout their service life.
Dr. Rui Yuan
Prof. Dr. Weihang Zhu
Dr. Xingkai Yang
Dr. Zhuo Long
Guest Editors
Hongan Wu
Guest Editor Assistant
Manuscript Submission Information
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Keywords
- multivariate entropy
- multivariate signal processing
- fault diagnosis
- structural health monitoring
- prognostics and health management
- remaining useful life prediction
- multi-sensor data fusion
- information-theoretic measures
- nonstationary heterogeneous signals
- intelligent condition monitoring
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