Nonlinear modeling for bearing fault diagnosis in non-stationary operating conditions

Bearing failure is one of the most important causes of shutdown in rotating machines. Most bearing diagnostic methods can only be used on machines with steady-state operational conditions. Changes in operating conditions cause changes in the statistical characteristics of the vibrating signals, which causes erroneous alarms related to bearing failure. The statistical index of vibration signals, independent of operating conditions of speed and load, is introduced in this paper to diagnosis bearing fault growth and reduce the rate of incorrect bearing failure alarms. The existing theoretical model is employed to simulate and extract the vibration database under variable operational conditions and finally, to extract the statistical index according to vibration surface plot parameters. Surface plots of acceleration RMS versus speed and load were used for extract statistical index and experimental laboratory data are also used to verify the proposed index.