The Application of Information Theory in Fault Detection and Diagnosis
This special issue belongs to the section "Signal and Data Analysis".
Special Issue Information
Dear Colleagues,
Today, instrumentation plays an integral role within Industry 4.0. Regarding maintenance and condition monitoring, the state of the art demands the detection of faults or possible failures in the short term, requiring the development of signal processing and decision making algorithms.
The theory of instrumentation and power system engineering is linked with the general information theory and Shannon entropy. The proposed Special Issue will cover advanced research in instrumentation and signal processing for the detection, diagnosis, and classification of faults in power systems, transmission lines, induction machines, electromechanical systems, and power quality disturbances.
This Special Issue provides a forum for the presentation of new and improved techniques for signal processing applied to fault detection and classification in power systems and industrial machines based on information theory, entropy, and machine learning.
Dr. José de Jesús Rangel-Magdaleno
Guest Editor
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Keywords
- fault diagnosis and prognosis
- application of entropy in instrumentation and fault diagnosis
- application of entropy in power systems for fault diagnosis
- intelligent instrumentation
- artificial intelligence and IoT in instrumentation
- compressed sensing
- early detection of incipient faults
- signal processing for monitoring and diagnosis
- information theory for patterns classification
- multi-sensor information fusion for instrumentation and fault diagnosis
- embedded systems for information theory processing
- machine learning for fault detection and classification
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Published Papers
Entropy - ISSN 1099-4300