抽象的

Rotor Unbalance Fault Diagnosis in Wind Turbine by Markov Chain Monte Carlo Method

Patricio Corbalan Campos, Luciano E. Chiang

An effective method based on current signal (of stator current) monitoring in time is applied to diagnosis rotor unbalanced or eccentricity fault. This method can also determine the degree of severity of the fault, which is determined in eccentric mass or eccentric radius. The experiment consisted in amplitude of current signal monitoring for various masses and radius that were applied. The eccentricity fault was simulated through a mass that was fixed in a shaft fixed to pulley solidarity to the turbine shaft, in this way, the health condition was evaluated for different position of the mass and different masses. The results were analyzed in curve between current amplitude and eccentric mass or current amplitude and eccentric radius. In both cases, the obtained tendency was a linear model, which verified the physical model of this situation. Bayesian method can estimate the current signal amplitude associated with a specific combination of mass and radius, determining the severity of the fault. Bayesian method can isolate the signal and the noise. The Bayesian method permits to prognosis the state of the turbine through amplitude of the current signal monitoring. This method was studied in stationary conditions.