Model Based Diagnosis of Malfunction in Rotor Systems using Genetic Algorithms and Equivalent Loads Method

A model based method for diagnosis of malfunction in rotor systems using equivalent loads and genetic algorithm method is presented. The presence of the fault changes the dynamic behavior of the system, and this change is taken into account by equivalent loads acting on the undamaged system model. Equivalent loads are fictitious forces and moments acting in the model of the undamaged system, which generates a dynamic behavior identical to that of the real damaged system. The mathematical representation of the equivalent loads is referred to as fault model. The identification of the fault is treated as an inverse problem, where the parameters of the fault are formulated as an optimization problem through the method of the equivalent loads, and the method of the genetic algorithms is implemented to search for the best estimate of these fault parameters. Genetic algorithms are stochastic search algorithms based on the mechanics of nature selection and natural genetics, which is designed to efficiently search large, non-linear, discrete and poorly-understood search space, where expert knowledge is scarse or difficult to model and where classical optimization techniques are limited in solving most of the inverse problems found in dynamic systems. A typical fault due to the unbalance was considered in this work. Numerical simulations and experimental results were accomplished for different rotations and measurement configurations. The results indicate that the method behaved very well in the identification of the fault for unbalance, demonstrating your potentiality for application the other types of faults.

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