Rolling Bearing Condition Monitoring Technique Based on Cage Rotation Analysis and Acoustic Emission

In this paper,we present an alternative technique for detecting changes in the operating conditions of rolling element bearings(REBs)that can lead to premature failure.The developed technique is based on measuring the kinematics of the bearing cage.The rotational motion of the cage is driven by traction forces generated in the contacts of the rolling elements with the races.It is known that the cage angular frequency relative to shaft angular frequency depends on the bearing load,the bearing speed,and the lubrication condition since these factors determine the lubricant film thickness and the associated traction forces.Since a large percentage of REB failures are due to misalignment or lubrication problems,any evidence of these conditions should be interpreted as an incipient fault.In this paper,a novel method for the measurement of the instantaneous angular speed(IAS)of the cage is developed.The method is evaluated in a deep groove ball bearing test rig equipped with a cage IAS sensor,as well as a custom acoustic emission(AE)transducer and a piezoelectric accelerometer.The IAS of the cage is analyzed under different bearing loads and shaft speeds,showing the dependence of the cage angular speed with the calculated lubricant film thickness.Typical bearing faulty operating conditions(mixed lubrication regime,lubricant depletion,and misalignment)are recreated.It is shown that the cage IAS is dependent on the lubrication regime and is sensitive to misalignment.The AE signal is also used to evaluate the lubrication regime.Experimental results suggest that the proposed technique can be used as a condition monitoring tool in industrial environments to detect abnormal REB conditions that may lead to premature failure.

Online Unbalance Detection and Diagnosis on Large Flexible Rotors by SVR and ANN trained by Dynamic Multibody Simulations

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.