Recognition of Rolling Bearing Life Status Based on FNER Performance Degradation Indicator and IDRSN

doi:10.3901/JME.2021.15.105

Abstract

Abstract: Aiming at the difficulty in evaluating the degradation performance of rolling bearing and identifying 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. Firstly, Hilbert transform and fast fourier transform (FFT) 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. The feature energy ratio of the envelope spectrum amplitude is calculated according to the fault characteristic frequency and its frequency multiplication. Subsequently, the total energy of the signal is obtained according to the autocorrelation function (AF). After that, the ratio of the fault feature energy and the noise energy is used as the bearing performance degradation indicator. Moreover, the bearing running state is 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 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 (GPA). Finally, two life cycle data sets of rolling bearings are used to verify the practicability and validity of FNER indicator and IDRSN model. The results show that the proposed method can more accurately identify the life state of rolling bearings.

Key words: feature-to-noise energy ratio, rolling bearing performance degradation assessment, early fault detection, improved deep residual shrinkage network, life state recognition

CLC Number:

TH17
TN911

DONG Shaojiang, PEI Xuewu, TANG Baoping, TIAN Kewei, ZHU Peng, LI Yang, ZHAO Xingxin. Recognition of Rolling Bearing Life Status Based on FNER Performance Degradation Indicator and IDRSN[J]. Journal of Mechanical Engineering, 2021, 57(15): 105-115.

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