Genetic Algorithms Based Rubbing Location Identification in a Rotor System

Wen Xiu Lu; Fu Lei Chu; Dan Guo

doi:10.4028/www.scientific.net/KEM.293-294.417

Paper Titles

Magnetic Bearings for Non-Destructive Health Monitoring of Rotating Machinery Supported in Conventional Bearings
p.383

Assessment of Materials and Operational Imperfections in Rotating Machinery
p.391

Modeling and Analyzing of Vibration in Working Crankshaft with Cracks
p.401

Stability Analysis of a Flexible Bladed-Rotor
p.409

Genetic Algorithms Based Rubbing Location Identification in a Rotor System
p.417

Bush Skewness Defect in Journal Bearing as an Example of Knowledge Base Development
p.425

Acceptable Dislocation of Bearings of the Turbine Set Considering Permissible Vibration and Load of the Bearings
p.433

Effect of Shaft Unbalance on the Operation of Worm Gear Rolling Bearings
p.441

Vibration Behaviour of a Disc Assembly with a Cracked Lacing Wire
p.451

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Genetic Algorithms Based Rubbing Location Identification in a Rotor System

Abstract:

The rubbing fault is a very serious and frequent malfunction in rotating machinery, and the determination of the rubbing location is very important in actual fault diagnosis. In this paper, a method based on genetic algorithms to detect the rubbing location is presented. The finite element model of the rubbing rotor is established with the rubbing location, the stator stiffness, the clearance between stator and rotor, the damping coefficient and the friction coefficient as the fault parameters, and the rubbing location determination is transferred into the parameter identification problem. The genetic algorithm is then utilized to search the solution. Using genetic algorithms avoids some of the weaknesses of traditional parameter identification methods such as local minimum problem in nonlinear system identification. The experimental results suggest that the rubbing location can be effectively determined when the rubbing occurs.