Rolling Bearing Fault Diagnosis based on Residual Neural Network
Abstract
Keywords
Full Text:
PDFReferences
Wang B, Lei Y, Li N, et al. Deep separable convolutional network for remaining useful life prediction of machinery. Mechanical Systems and Signal Processing 2019; 134.
Lu L, Yan J, De Silva CW. Dominant feature selection for the fault diagnosis of rotary machines using modified genetic algorithm and empirical mode decomposition. Journal of Sound and Vibration 2015; 344.
Lu C, Wang Z, Qin W, et al. Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Processing 2017; 130: 377–388.
Zhang X, Liang Y, Zhou J, et al. A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM. Measurement 2015; 69: 164–179.
Yan X, Jia M. A novel optimized SVM classification algorithm with multi-domain feature and its
application to fault diagnosis of rolling bearing. Neurocomputing 2018; 313: 47–64.
Wang L, Liu Z, Miao Q, et al. Time-frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis. Mechanical Systems and Signal Processing 2018; 103: 60–75.
Mao W, He L, Yan Y, et al. Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine. Mechanical Systems and Signal Processing 2017; 83: 450–473.
Xiang Z, Zhang X, Zhang W, et al. Fault diagnosis of rolling bearing under fluctuating speed and variable load based on TCO spectrum and stacking auto-encoder. Measurement 2019; 138: 162–174.
Zhang W, Peng G, Li C, et al. A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors 2017; 17(2): 425.
Chen Z, Gryllias K, Li W. Mechanical fault diagnosis using convolutional neural networks and extreme learning machine. Mechanical Systems and Signal Processing 2019; 133.
Jing L, Zhao M, Li P, et al. A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox. Measurement 2017; 111: 1–10.
Zhang W, Li C, Peng G, et al. A deep convolutional neural network with new training methods
for bearing fault diagnosis under noisy environment and different working load. Mechanical Systems and Signal Processing 2018; 100: 439–453.
Zhang K, Cheng J, Yang Y. Roller bearing fault diagnosis based on local mean decomposition and morphological fractal dimension. Journal of Vibration and Shock 2013; 32(9): 90–94.
He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition; 2016.
Yu F, Koltun K. Multi-scale context aggregation by dilated convolutions. ICLR; 2016.
DOI: https://doi.org/10.18282/fme.v2i4.1547
Refbacks
- There are currently no refbacks.