Entropy-Based Fault Diagnosis and Health Monitoring of Intelligent Engineering Systems
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".
Deadline for manuscript submissions: 31 December 2027 | Viewed by 155
Editors
Interests: prognostics and health management; artificial intelligence and pattern recognition; intelligent fault diagnosis
Interests: fault diagnosis; signal processing; rotating machinery dynamics
Special Issues, Collections and Topics in MDPI journals
Interests: mechanical dynamics; fault diagnosis; artificial intelligence; intelligent operation and maintenance
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the rapid development of intelligent equipment, cyber–physical systems, and data-driven maintenance technologies, fault diagnosis and health monitoring have become essential for ensuring the reliability, safety, and efficiency of complex engineering systems. These systems are widely used in aerospace, transportation, industrial manufacturing, energy equipment, marine engineering, robotics, and other safety-critical fields. Their increasing complexity, multi-source sensing structures, and dynamic operating conditions bring new challenges to fault detection, diagnosis, prognosis, and decision-making.
Entropy and information-theoretic methods provide powerful tools for characterizing uncertainty, complexity, disorder, and information evolution in signals and system states. They have been widely applied to feature extraction, anomaly detection, pattern recognition, degradation assessment, remaining useful life prediction, and intelligent maintenance. In recent years, the integration of entropy-based measures with advanced signal processing, statistical learning, machine learning, deep learning, transfer learning, and multi-sensor information fusion has opened new opportunities for reliable and interpretable fault diagnosis under complex operating environments.
This Special Issue aims to collect high-quality original research papers and review articles on entropy-driven theories, methods, and applications for fault diagnosis, health monitoring, and prognosis of engineering systems. Topics of interest include, but are not limited to, entropy-based signal analysis, intelligent fault diagnosis, condition monitoring, degradation modeling, data-driven prognostics, machine learning and deep learning methods, and applications in aerospace systems, rail transit, vehicles, industrial equipment, rotating machinery, power systems, marine equipment, and other complex engineering systems.
Dr. Zhenya Wang
Dr. Jinfeng Huang
Dr. Feibin Zhang
Dr. Zhilin Dong
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.
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
- entropy theory
- information theory
- fault diagnosis
- condition monitoring
- health management
- fault prognostics
- signal processing
- machine learning
- deep learning
- engineering systems
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