Intelligent Rolling Bearing Fault Diagnosis under Variable Speed Conditions without Tachometers

Under variable speed conditions, vibration signal of rolling bearings exhibits non-stationary characteristics, which makes traditional stationary fault diagnosis methods no longer applicable. In this study, an intelligent fault diagnosis method for rolling bearings under variable speed and tacholess conditions is proposed. First, with the use of instantaneous phase of the shaft estimated by variational nonlinear chirp mode decomposition (VNCMD), the collected non-stationary time-domain vibration signals and their envelope can be resampled and transformed into stationary angle-domain signals by tacholess order tracking (TLOT). Then, one-dimensional signals are converted to two-dimensional images by dimension increase, normalization and image compression. Finally, the fault type of rolling bearings is obtained utilizing improved LeNet-5 convolutional neural network (CNN). Results on experimental data of rolling bearings under variable speed conditions demonstrate the proposed method is more effective than traditional stationary methods.