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Smart Monitoring of Rotating Machinery for Industry 4.0

  • Book
  • © 2022

Overview

Editors:
  1. Fakher Chaari

    1. Mechanics Modelling and Production Lab, National School of Engineers of Sfax, Sfax, Tunisia

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  2. Xavier Chiementin

    1. Institut de Thermique, Mécanique, et Matériaux (ITheMM EA7548), University of Reims Champagne-Ardenne, Reims, France

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  3. Radoslaw Zimroz

    1. Faculty of GeoEngineering Mining and Geology, Wrocław University of Technology, Wrocław, Poland

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  4. Fabrice Bolaers

    1. Institut de Thermique, Mécanique, et Matériaux (ITheMM EA7548), University of Reims Champagne-Ardenne, Reims, France

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  5. Mohamed Haddar

    1. National School of Engineers of Sfax, Sfax, Tunisia

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  • Reports on intelligent methods for condition monitoring of rotating machinery
  • Presents strategies for managing large dataset in machine diagnostics
  • Offers a good balance of theoretical and practical issues

Part of the book series: Applied Condition Monitoring (ACM, volume 19)

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About this book

This book offers an overview of current methods for the intelligent monitoring of rotating machines. It describes the foundations of smart monitoring, guiding readers to develop appropriate machine learning and statistical models for answering important challenges, such as the management and analysis of a large volume of data. It also discusses real-world case studies, highlighting some practical issues and proposing solutions to them. The book offers extensive information on research trends, and innovative strategies to solve emerging, practical issues. It addresses both academics and professionals dealing with condition monitoring, and mechanical and production engineering issues, in the era of industry 4.0.

Keywords

Table of contents (11 chapters)

  1. Front Matter

    Pages i-vi
    Download chapter PDF

  2. Vulnerabilities and Fruits of Smart Monitoring

    • Jablonski Adam, Tomasz Barszcz
    Pages 1-9

  3. A Tutorial on Canonical Variate Analysis for Diagnosis and Prognosis

    • Xiaochuan Li, Tianran Lin, David Mba
    Pages 11-31

  4. A Structured Approach to Machine Learning Condition Monitoring

    • Luca Capelli, Giulia Massaccesi, Jacopo Cavalaglio Camargo Molano, Federico Campo, Davide Borghi, Riccardo Rubini et al.
    Pages 33-54

  5. A Structured Approach to Machine Learning for Condition Monitoring: A Case Study

    • Jacopo Cavalaglio Camargo Molano, Federico Campo, Luca Capelli, Giulia Massaccesi, Davide Borghi, Riccardo Rubini et al.
    Pages 55-76

  6. Dynamic Reliability Assessment of Structures and Machines Using the Probability Density Evolution Method

    • Sajad Saraygord Afshari, Ming J. Zuo, Xihui Liang
    Pages 77-101

  7. Rotating Machinery Condition Monitoring Methods for Applications with Different Kinds of Available Prior Knowledge

    • Stephan Schmidt, P. Stephan Heyns
    Pages 103-115

  8. Model Based Fault Diagnosis in Bevel Gearbox

    • Palash Dewangan, Dada Saheb Ramteke, Anand Parey
    Pages 117-133

  9. Investigating the Electro-mechanical Interaction Between Helicoidal Gears and an Asynchronous Geared Motor

    • Safa Boudhraa, Alfonso Fernandez del Rincon, Mohamed Amine Ben Souf, Fakher Chaari, Mohamed Haddar, Fernando Viadero
    Pages 135-145

  10. Algebraic Estimator of Damping Failure for Automotive Shock Absorber

    • Maroua Haddar, Riadh Chaari, S. Caglar Baslamisli, Fakher Chaari, Mohamed Haddar
    Pages 147-155

  11. On the Use of Jerk for Condition Monitoring of Gearboxes in Non-stationary Operations

    • Fakher Chaari, Stephan Schmidt, Ahmed Hammami, P. Stephan Heyns, Mohamed Haddar
    Pages 157-167

  12. Dynamic Remaining Useful Life Estimation for a Shaft Bearings System

    • Mohamed Habib Farhat, Fakher Chaari, Xavier Chiementin, Fabrice Bolaers, Mohamed Haddar
    Pages 169-178
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Editors and Affiliations

  • Mechanics Modelling and Production Lab, National School of Engineers of Sfax, Sfax, Tunisia

    Fakher Chaari

  • Institut de Thermique, Mécanique, et Matériaux (ITheMM EA7548), University of Reims Champagne-Ardenne, Reims, France

    Xavier Chiementin, Fabrice Bolaers

  • Faculty of GeoEngineering Mining and Geology, Wrocław University of Technology, Wrocław, Poland

    Radoslaw Zimroz

  • National School of Engineers of Sfax, Sfax, Tunisia

    Mohamed Haddar

About the editors

Prof. Fakher Chaari, National School of Engineers of Sfax, Mechanics Modelling & Production Lab, Sfax, Tunisia


Prof. Xavier Chiementin, 
University of Reims Champagne-Ardenne, Institut de Thermique, Mécanique,Reims, France


Prof. Radoslaw Zimroz, 
Wrocław University of Technology, Faculty of Geo Engineering. Mining and Geology, Wrocław, Poland


Prof. Fabrice Bolaers, 
University of Reims Champagne-Ardenne, Institut de Thermique, Reims, France


Prof. Mohamed Haddar, 
National School of Engineers of Sfax, Sfax, Tunisia


Bibliographic Information

  • Book Title: Smart Monitoring of Rotating Machinery for Industry 4.0

  • Editors: Fakher Chaari, Xavier Chiementin, Radoslaw Zimroz, Fabrice Bolaers, Mohamed Haddar

  • Series Title: Applied Condition Monitoring

  • DOI: https://doi.org/10.1007/978-3-030-79519-1

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

  • Hardcover ISBN: 978-3-030-79518-4Published: 21 August 2021

  • Softcover ISBN: 978-3-030-79521-4Published: 22 August 2022

  • eBook ISBN: 978-3-030-79519-1Published: 20 August 2021

  • Series ISSN: 2363-698X

  • Series E-ISSN: 2363-6998

  • Edition Number: 1

  • Number of Pages: VI, 178

  • Number of Illustrations: 7 b/w illustrations, 100 illustrations in colour

  • Topics: Machinery and Machine Elements, Signal, Image and Speech Processing, Complexity

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