Entropy in Fault Diagnosis
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 861
Special Issue Editors
Interests: development of numerical methods; modelling of optical effects; visual cryptography; nonlinear dynamical systems and chaos
Special Issues, Collections and Topics in MDPI journals
Interests: structural health monitoring; structural damage identification; smart materials and structures; applied soft computing; structural vibration and control
Special Issues, Collections and Topics in MDPI journals
Interests: structural health monitoring; structural damage identification; structural dynamics; vibro-acoustics
Special Issues, Collections and Topics in MDPI journals
2. Mechanical Engineering, Rowan University, Glassboro, NJ, USA
Interests: vibro- acoustics engineering and applications; structural health monitoring; elastic wave propagation; composites & smart structure; numerical modeling& analysis
Special Issue Information
Dear Colleagues,
Intelligent signal processing algorithms and analysis methods play a pivotal role in fault diagnosis. For accurate fault diagnosis, data must be preprocessed and turned into a convenient form before knowledge can be acquired.
A wide class of fault detection algorithms are entropy-based techniques. However, the direct application of entropy-based methods for feature extraction may not prevent the side effects of noise in the data. Therefore, entropy-based methods are often used as a fault detection technique only after initial feature extraction techniques.
This Special Issue aims to aggregate the latest research results contributing to theoretical, methodological, and technological advances in using entropy-based methods for the detection of anomalies, forecasting potential degradation, and classifying faults from complex environments and signals. High-quality theoretical and application papers dealing with various fault detection and diagnosis problems using the entropy-based approach alone or in combination with other approaches are also welcomed.
Prof. Dr. Minvydas Ragulskis
Prof. Dr. Maosen Cao
Prof. Dr. Rafal Burdzik
Prof. Dr. Vinayak Ranjan
Dr. Grigory Panovko
Guest Editors
Manuscript Submission Information
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Keywords
- entropy-based methods
- feature extraction
- deep learning
- fault diagnosis
- remaining useful life
- predictive maintenance
