Estimation of defect severity in rolling element bearings using vibration signals with artificial neural network

In condition monitoring of rotating machinery,vibration analysis is a popularly used diagnostic tool for checking the health of rolling element bearings. the vibration signal caused by bearing defects will always be contaminated and distorted by other faults and mechanical noise particularly in hostile environment. vibration based methods are effective when the defect in the bearings has already become severe. a bearing test rig was designed and setup in a workshop to study the vibration analysis of various faults in rolling element bearings. in the literature,it was observed that researchers studied different types of seeded defects,but these defects are random in size and shape; hence the correlation between defect size and its vibration parameter is not established. in this investigation,test runs conducted with seeded defects of same type with a gradual increase of its size on outer race of radially loaded cylindrical roller bearings at different speeds and loads. vibration data were acquired by accelerometer and processed through fast fouier transform (fft). from the data,it was found that the vibration root mean square (rms) velocity increases significantly with the increase of defect size and speed,but not with the load. artificial neural networks (ann) multilayer perception model with back-propagation algorithm was used,with input parameters of load,revolutions per minute (rpm) and vibration rms velocity and output is seeded defect size. the ann was trained with data sets of number of test runs conducted and predicted the defect size. the predicted values were compared with the actual seeded defect size and found the error was approximately 3.90%. in this investigation,an attempt was made to predict the defect size of a specific bearing with respect to its vibration rms velocity for given conditions. this study may be useful for the monitoring of critical bearings in the industry. © 2015 jordan journal of mechanical and industrial engineering.