http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Proceedings of the 10th International Conference on Rotor Dynamics - IFToMM
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. .
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.
edited by Katia Lucchesi Cavalca, Hans Ingo Weber.