Special Issue "Permutation Entropy - Theory, Algorithms and Applications in Predictive Maintenance and Machine Fault Diagnosis"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (16 December 2021).

Special Issue Editors

Prof. Dr. Minvydas Ragulskis
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Guest Editor
Center for Nonlinear Systems, Kaunas University of Technology, Studentu, 50-147 Kaunas, Lithuania
Interests: nonlinear dynamics; time series analysis; permutation entropy; pattern recognition; big data and deep learning
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Prof. Dr. Maosen Cao
E-Mail Website
Guest Editor
1. Jiangxi Provincial Key Laboratory of Environmental Geotechnical Engineering and Disaster Control, Jiangxi University of Science and Technology, Ganzhou 341000, China
2. Institute of Dynamics and Control, College of Mechanics and Materials, Hohai University, Nanjing 210098, China
Interests: predictive fault diagnosis; machine learning techniques; big data and deep learning; structural health monitoring, uncertainty analysis
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Prof. Dr. Rafal Burdzik
E-Mail Website
Guest Editor
Faculty of Transport, Silesian University of Technology, Katowice, Poland
Interests: acoustics and vibration; vibration measurement and control; predictive maintenance; transportation science and engineering, numerical simulations
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Prof. Dr. Vinayak Ranjan
E-Mail Website1 Website2
Guest Editor
1. Mechanical and Aerospace Engineering, Bennett University, Uttar Pradesh, India
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
Dr. Grigory Panovko
E-Mail Website
Guest Editor
Mechanical Engineering Research Institute of Russian Academy of Sciences, Moscow, Russia
Interests: nonlinear dynamics; mechanical and structural engineering, predictive maintenance; remaining useful life; vibration engineering

Special Issue Information

Dear Colleagues,

Intelligent signal processing algorithms and analysis methods play a pivotal role in machinery 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 permutation entropy (PE)-based techniques. However, direct application of PE for feature extraction may not avoid the side effects of noise in the data. Therefore, PE is often used as a fault detection technique only after initial feature extraction techniques.

PE itself has inherent imperfections as a method: the amplitude information of the time series is not considered by PE. Thus, other kinds of entropy measures like multiscale dispersion entropy, fuzzy measure entropy, or generalized refined composite multi-scale sample entropy are also used in predictive maintenance and machine fault diagnosis applications. Many different variants of the standard PE have been introduced and implemented in a variety of applications during the recent years: multi-scale PE, multi-scale weighted PE, phase PE, improved PE, generalized PE, spatiotemporal PE, fine-grained PE, refined composite PE.

This special issue aims to aggregate the latest research results contributing to theoretical, methodological, and technological advances in using PE for the detection of anomalies, forecasting potential degradation, and classifying faults from complex environments and signals.

The following topics are of a primary interest for this special issue:

  • PE in predictive maintenance (theory, algorithms and applications)
  • PE-based algorithms for RUL (remaining useful life) prediction
  • PE in machine learning and artificial intelligence-based fault diagnosis and prediction
  • PE-based feature extraction algorithms and machine fault applications
  • PE for analysis of data streams from sensor arrays in monitoring applications
  • PE-based sparse data analysis algorithms for machine fault diagnosis and monitoring

Prof. Dr. Minvydas Ragulskis
Prof. Dr. Maosen Cao
Prof. Dr. Rafal Burdzik
Prof. Dr. Vinayak Ranjan
Dr. Grigory Panovko
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 papers will be 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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind 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 1800 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

  • permutation entropy
  • feature extraction
  • deep learning
  • intelligent fault diagnosis
  • remaining useful life
  • predictive maintenance

Published Papers

There is no accepted submissions to this special issue at this moment.
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