Bearing Running State Recognition Method Based on Feature-to-Noise Energy Ratio and Improved Deep Residual Shrinkage Network

Abstract

Aiming at the difficulty in evaluating the degradation performance of rolling bearing and recognizing bearing running state, a new method based on feature-to-noise energy ratio (FNER) indicator and improved deep residual shrinkage network (IDRSN) model is proposed. First, Hilbert transform and fast Fourier transform are used to obtain the envelope spectrum of bearing run-to-failure test signal and the fault feature energy of the envelope spectrum amplitude is calculated. Subsequently, the total energy of the envelope signal is obtained according to the autocorrelation function (ACF). After that the ratio of the fault feature energy and the noise energy is used as the bearing performance degradation indicator. Moreover, bearing running states are divided according to FNER indicator and tagged bearing samples realized. Then, tagged samples are used to train the IDRSN, which introduces the densely connected network (DenseNet) to obtain the bearing running state recognition model. In order to improve the anti-interference ability, the DropBlock layer is introduced into the first large convolution kernel and the Dropout technique is introduced before the global average pooling layer. Finally, a failure test data is used to verify the feasibility of the FNER indicator and two bearing run-to-failure test data are used to verify the validity of the FNER indicator and the IDRSN recognition model. Through comparison and verification, the results show that the proposed FNER indicator is better than other indicators and IDRSN can more accurately recognize bearing running state recognition.