Model Based Robust Balancing Approach for Rotating Machines
Unbalance is one of the most common problems found in rotating machines in the context of industrial plants. This phenomenon can generate high vibration amplitudes that can lead to the fatigue of rotor elements. Various approaches have been proposed in order to solve the rotor balancing problem, as the so-called influence coefficients technique. This paper presents an alternative balancing methodology for rotating machines, aiming at overcoming the limitations faced by the frequently used methods. This alternative technique first identifies the model of the machine, and then the unbalance is determined by solving a typical inverse problem through an optimization method by taking into account the inherent uncertainties that affect the balancing performance. The robust balancing methodology is based on a multi-objective fuzzy optimization procedure, in which the uncertainties are treated as fuzzy variables. The robust optimum is determined by using an objective function which minimizes a predefined robustness metric. Finally, the numerical investigation is applied to a rotor composed by a horizontal flexible shaft, two rigid discs, and two ball bearings. The results indicate the effectiveness of the proposed technique.
KeywordsRotordynamics Balancing Fuzzy logic Robust optimization Decision making under uncertainties
The authors gratefully acknowledge the Brazilian Research Agencies FAPEMIG, CNPq, and CAPES for the financial support provided to this research effort through the National Institute of Science and Technology of Smart Structures in Engineering (INCT-EIE).
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