Proceedings of the 10th International Conference on Rotor Dynamics - IFToMM (eBook, PDF)

Vol. 2
Redaktion: Cavalca, Katia Lucchesi; Weber, Hans Ingo

• Format: PDF

Produktbeschreibung

IFToMM conferences have a history of success due to the various advances achieved in the field of rotor dynamics over the past three decades. These meetings have since become a leading global event, bringing together specialists from industry and academia to promote the exchange of knowledge, ideas, and information on the latest developments in the dynamics of rotating machinery.

The scope of the conference is broad, including e.g. active components and vibration control, balancing, bearings, condition monitoring, dynamic analysis and stability, wind turbines and generators, electromechanical interactions in rotor dynamics and turbochargers.

The proceedings are divided into four volumes. This second volume covers the following main topics: condition monitoring, fault diagnostics and prognostics; modal testing and identification; parametric and self-excitation in rotor dynamics; uncertainties, reliability and life predictions of rotating machinery; and torsional vibrations and geared systems dynamics.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 576
• Erscheinungstermin: 18. August 2018
• Englisch
• ISBN-13: 9783319992686
• Artikelnr.: 53615310

Autorenporträt

Prof. Dr. Katia Lucchesi Cavalca is a full professor (MS6) at the University of Campinas in Brazil. She has been a member of the IFToMM Technical Committee for Rotordynamics since 2004 and has been deputy chair of the committee since 2012. In 2014 she was invited to join the National Academy of Engineering. She is currently Chair of the TC Rotordynamics (2018-2019), and Technical Editor for the Mechanism and Machine Theory journal, inTribology and Rotordynamics topics. She has extensive experience in Mechanical Engineering, particularly in the areas of Dynamics and Machinery Design and the following topics: rotor dynamics, lubricated bearings, modeling, and vibration analysis in rotating machinery. Prof. Dr. Hans Ingo Weber holds a degree in Mechanical Engineering from the University of São Paulo (1966), a Master's in Mechanical Engineering from the Federal University of Rio de Janeiro (1968) and a PhD in Mechanical Engineering from the Technische Universitat Munchen (1971). He is currently professor emeritus of the Pontifical Catholic University of Rio de Janeiro. He was an Assistant Professor at COPPE/UFRJ from 1968 to 1974, and UNICAMP from 1974 to 1997, becoming a full professor in 1978. He was superintendent of the Technology Center of UNICAMP from 1990 to 1996. In 2008 he was invited to join the National Academy of Engineering, and was elected Secretary General on the Board of Directors in 2010. He has considerable experience in Mechanical Engineering, particularly in the Dynamics of Rigid, Elastic and Plastic Bodies and the following topics: rotation dynamics, nonlinear dynamics, drillstrings, chaos, impact and biomechanics.

Inhaltsangabe

Crack Detection and Dynamic Analysis of a Cracked Rotor with Soft Bearings Using Different Methods of Solution.- Nuclear Reactor Heat Transfer Pump Rotordynamic Based Diagnostics and field Data Model Reconciliation.- Performance Analysis of Support Vector Machine and Wavelet Packet Transform based Fault Diagnostics of Induction Motor at Various Operating Conditions.- Multi Fault Diagnosis of Centrifugal Pumps with Time, Frequency and Wavelet based Features using Support Vector Machines.- Interpretation of Dynamic Data Plots for Troubleshooting and Resolving Vibration in Large Rotating Machinery.- Prediction of Dynamic Characteristics Induced by Rubbing Between Rotating Blade and Casing.- Application of Machine Learning in Diesel Engines Fault Identification.- Detection of Cracks in a Rotating Shaft Using Density Characterization of Orbit Plots.- Model-Based Vibration Condition Monitoring for Fault Detection and Diagnostics Applied to Large Hydro Generators.- Monitoring of Induction Motor Mechanical and Electrical Faults by Optimum Multiclass-Support Vector Machine Algorithms Using Genetic Algorithm.- Application of Deep Stacked Auto-Encoder Neural Networks to Feature Learning for Intelligent Diagnosis of Machine Condition.