Vibration Images‐Driven Fault Diagnosis Based on CNN and Transfer Learning of Rolling Bearing under Strong Noise

Abstract

Deep learning-based fault diagnosis of rolling bearings is a hot research topic, and a rapid and accurate diagnosis is important. In this paper, aiming at the vibration image samples of rolling bearing affected by strong noise, the convolutional neural network- (CNN-) and transfer learning- (TL-) based fault diagnosis method is proposed. Firstly, four kinds of vibration image generation method with different characteristics are put forward, and the corresponding pure vibration image samples are obtained according to the original data. Secondly, using CNN as the adaptive feature extraction and recognition model, the influences of main sensitive parameters of CNN on the network recognition effect are studied, such as learning rate, optimizer, and L1 regularization, and the best model is determined. In order to obtain the pretraining parameters, the training and fault classification test for different image samples are carried out, respectively. Thirdly, the Gaussian white noise with different levels is added to the original signals, and four kinds of noised vibration image samples are obtained. The previous pretrained model parameters are shared for the TL. Each kind of sample research compares the impact of thirteen data sharing schemes on the TL accuracy and efficiency, and finally, the test accuracy and time index are introduced to evaluate the model. The results show that, among the four kinds of image generation method, the classification performance of data obtained by empirical mode decomposition-pseudo-Wigner–Ville distribution (EP) is the best; when the signal to noise ratio (SNR) is 10 dB, the model test accuracy obtained by TL is 96.67% and the training time is 170.46 s.

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

“…Wang_2021 [233] Rauber_2021 [219] Fan_2021 [234] Qian_2020 [235] Zhao_2020a [236] Chen_2020 [237] Li_2020 [238] Xin_2020 [239] Li_2019 [240] Hoang_2019 [241] Qian_2018 [242] Qian_2018 [242] Bai_2021b [243] Sharma_2021 * [25]…”

“…A variety of models are proposed for fault diagnosis and, among these, convolutional models (CNNs) have become one of the most popular deep learning methods because they support diverse input data, not only vectors, e.g., images. Some examples applied to rolling bearing diagnostics are: MIMTNet, a CNN with multi-dimensional signal inputs and multi-dimensional task outputs, proposed by Wang et al in 2021 [233]; the CNN and transfer learning (TL) based fault diagnosis method proposed by Fan et al in 2021 [234]; a one-dimensional-CNN and a dilated CNN, obtained through the combination between a CNN and an automatic hyper-parametric optimization, proposed by Li et al in 2020 [238]. The deep morphological CNN (DMCNet), where a morphological filter is used to implement noise reduction and impulsive component extraction, and the multiscale CNN (MSCNN), with a novel morphological layer is smoothly embedded in DNN as a signal processing layer to extract impulses and filter out the noise, were presented by Ye and Yu in 2021, in [244,245], respectively; a multi-channels DCNN (MC-DCNN) was described by Kolar et al [246], with a high definition 1D image of raw three axes accelerometer signals as input, and the deep capsule network with stochastic delta rule (DCN-SDR) was presented by Chen et al in 2019 [269].…”

Hybrid deep transfer learning architecture for industrial fault diagnosis using Hilbert transform and DCNN–LSTM

“…In [19], a VGG16 pre-trained network was used to extract the lower-level features and label wavelet transform images. Fan et al [20] implemented TL in a CNN by generating texture images using empirical mode decomposition with the pseudo-Wigner-Ville distribution. The state-of-art DTL models used pre-trained ImageNet weights for implementing TL.…”

“…To overcome the limitations of DL models, transfer learning (TL) was incorporated into DL models so that knowledge from one problem can be used to solve a related different problem [4,[13][14][15][16][17][18][19][20]. For example, pre-trained ImageNet weights have been used to initialize the parameters in deep networks for classifying industrial faults instead of random initialization.…”

Intelligent data expansion approach of vibration gray texture images of rolling bearing based on improved WGAN-GP

“…Zhou and Yu 8 proposed 1D residual convolutional AE (Auto-Encoder) for the vibration feature extraction, and then used a small amount of labeled data for classification and fine tuning to complete fault diagnosis. For vibration images of rolling bearing affected by noise, Fan et al 9 used CNN as an adaptive feature extraction and recognition tool to study different vibration image samples. Zhang et al 10 proposed a multiscale holospectrum CNN based on 2D feature fusion and decision-level fusion.…”