Multiple-stage steam turbine generators,like those found in nuclear power plants,pose special challenges with regards to mechanical unbalance diagnosis.Several factors contribute to a complex vibrational response,whic...Multiple-stage steam turbine generators,like those found in nuclear power plants,pose special challenges with regards to mechanical unbalance diagnosis.Several factors contribute to a complex vibrational response,which can lead to incorrect assessments if traditional condition monitoring strategies are used without considering the mechanical system as a whole.This,in turn,can lead to prolonged machinery downtime.Several machine learning techniques can be used to integrally correlate mechanical unbalance along the shaft with transducer signals from rotor bearings.Unfortunately,this type of machinery has scarce data regarding faulty behavior.However,a variety of fault conditions can be simulated in order to generate these data using computational models to simulate the dynamic response of individual machines.In the present work,a multibody model of a 640MWsteam turbine flexible rotor is employed to simulate mechanical unbalance in several positions along the shaft.Synchronous components of the resulting vibration signals at each bearing are obtained and utilized as training data for two regression models designed for mechanical unbalance diagnosis.The first approach uses an artificial neural network and the second one utilizes a support vector regression algorithm.In order to test their performance,the stiffness of each bearing in the multibody simulation was altered between 50%and 150%of the training model values,random noise was added to the signal and several dynamic unbalance conditions were simulated.Results show that both approaches can reliably diagnose dynamic rotor unbalance even when there is a typical degree of uncertainty in bearing stiffness values.展开更多
文摘Multiple-stage steam turbine generators,like those found in nuclear power plants,pose special challenges with regards to mechanical unbalance diagnosis.Several factors contribute to a complex vibrational response,which can lead to incorrect assessments if traditional condition monitoring strategies are used without considering the mechanical system as a whole.This,in turn,can lead to prolonged machinery downtime.Several machine learning techniques can be used to integrally correlate mechanical unbalance along the shaft with transducer signals from rotor bearings.Unfortunately,this type of machinery has scarce data regarding faulty behavior.However,a variety of fault conditions can be simulated in order to generate these data using computational models to simulate the dynamic response of individual machines.In the present work,a multibody model of a 640MWsteam turbine flexible rotor is employed to simulate mechanical unbalance in several positions along the shaft.Synchronous components of the resulting vibration signals at each bearing are obtained and utilized as training data for two regression models designed for mechanical unbalance diagnosis.The first approach uses an artificial neural network and the second one utilizes a support vector regression algorithm.In order to test their performance,the stiffness of each bearing in the multibody simulation was altered between 50%and 150%of the training model values,random noise was added to the signal and several dynamic unbalance conditions were simulated.Results show that both approaches can reliably diagnose dynamic rotor unbalance even when there is a typical degree of uncertainty in bearing stiffness values.
