A New Transfer Learning Based on VGG-19 Network for Fault Diagnosis

Wen, Long; Li, X.; Li, Xinyu; Gao, Liang

doi:10.1109/cscwd.2019.8791884

Cited by 128 publications

(54 citation statements)

References 14 publications

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“…To further verify the effectiveness of the proposed rolling-element bearing fault diagnosis methods, the performance of improved 2D LeNet-5 network and improved 1D LeNet-5 network are compared with that of the other nine different fault diagnosis methods based on machine learning or deep learning, including SVM [8], k-NN [9], K-Means [10], BPNN [11], compact 1D CNN without fine-tuning [27], AlexNet [23], VGG-19 [24], ResNet-50 [25] and traditional LeNet-5 network [31].…”

Section: Comparison With Other Fault Diagnosis Methodsmentioning

confidence: 99%

Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network

Wan

Chen

et al. 2020

Sensors

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To address the problems of low recognition accuracy, slow convergence speed and weak generalization ability of traditional LeNet-5 network used in rolling-element bearing fault diagnosis, a rolling-element bearing fault diagnosis method using improved 2D LeNet-5 network is put forward. The following improvements to the traditional LeNet-5 network are made: the convolution and pooling layers are reasonably designed and the size and number of convolution kernels are carefully adjusted to improve fault classification capability; the batch normalization (BN) is adopted after each convolution layer to improve convergence speed; the dropout operation is performed after each full-connection layer except the last layer to enhance generalization ability. To further improve the efficiency and effectiveness of fault diagnosis, on the basis of improved 2D LeNet-5 network, an end-to-end rolling-element bearing fault diagnosis method based on the improved 1D LeNet-5 network is proposed, which can directly perform 1D convolution and pooling operations on raw vibration signals without any preprocessing. The results show that the improved 2D LeNet-5 network and improved 1D LeNet-5 network achieve a significant performance improvement than traditional LeNet-5 network, the improved 1D LeNet-5 network provides a higher fault diagnosis accuracy with a less training time in most cases, and the improved 2D LeNet-5 network performs better than improved 1D LeNet-5 network under small training samples and strong noise environment.Sensors 2020, 20, 1693 2 of 23 clustering [10], back propagation neural network (BPNN) [11], etc. The traditional rolling-element bearing fault diagnosis methods have been widely used, but with the increasing complexity of vibration signals, these methods have a certain limitation; however, the deep learning methods have a greater advantage in analyzing complicated and non-stationary vibration signals.The deep learning methods can automatically extract fault features from vibration signals [12], recently there are many researches are conducted on rolling-element bearing fault diagnosis using deep learning. Yin et al. [13] extracted the original features of vibration signals through time-domain analysis, frequency-domain analysis and wavelet transform, and obtained the low-dimensional features from 38 original features using the nonlinear global algorithm, and the low-dimensional features array is input into the deep belief network (DBN) to evaluate the performance status of rolling-element bearing. Liu et al. [14] obtained the spectrogram of vibration signals through STFT, used the stacked sparse auto-encoder (SAE) to automatically extract fault features, and employed the softmax regression to identify the fault type of rolling-element bearing. Liu et al. [15] used the recurrent neural network (RNN) to classify the faults of rolling-element bearing, and adopted the gated recurrent unit based denoising auto-encoder to enhance fault classification accuracy. Among different deep learning methods, compared with DBN, ...

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Section: Comparison With Other Fault Diagnosis Methodsmentioning

confidence: 99%

Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network

Wan

Chen

et al. 2020

Sensors

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“…However, one attractive benefit of recently popular deep learning methods is that they can extract important features on their own [35]. CNNs can take pixels of images as input, learn features using its hidden layers and finally classify using the extracted feature information [36,37]. CNNs have repeatedly demonstrated their superiority over manual feature engineering of images by breaking their benchmarks [11,[38][39][40].…”

Section: Methodsmentioning

confidence: 99%

“…We replace VGG-16 network with other pre-trained networks for the CNN| X f er module. VGG-19 [37], ResNet [40], NasNet [49], MobileNet [50], Inception Network [51], XceptionNet [52], and DenseNet [53] are the most used pre-trained networks in computer vision for transfer learning. VGG-19 network resembles the VGG-16 network most (which was used in our CNN| X f er module), except that it contains three more layers.…”

Section: Testing On Augmented Datasetmentioning

confidence: 99%

Convolutional Neural Network Based Ensemble Approach for Homoglyph Recognition

Majumder

Rahman

Iqbal

et al. 2020

MCA

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Homoglyphs are pairs of visual representations of Unicode characters that look similar to the human eye. Identifying homoglyphs is extremely useful for building a strong defence mechanism against many phishing and spoofing attacks, ID imitation, profanity abusing, etc. Although there is a list of discovered homoglyphs published by Unicode consortium, regular expansion of Unicode character scripts necessitates a robust and reliable algorithm that is capable of identifying all possible new homoglyphs. In this article, we first show that shallow Convolutional Neural Networks are capable of identifying homoglyphs. We propose two variations, both of which obtain very high accuracy (99.44%) on our benchmark dataset. We also report that adoption of transfer learning allows for another model to achieve 100% recall on our dataset. We ensemble these three methods to obtain 99.72% accuracy on our independent test dataset. These results illustrate the superiority of our ensembled model in detecting homoglyphs and suggest that our model can be used to detect new homoglyphs when increasing Unicode characters are added. As a by-product, we also prepare a benchmark dataset based on the currently available list of homoglyphs.

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“…There are a variety set of state-of-the-art CNN models that can be implemented as base-models for transfer learning such as VGG16 (Simonyan, and Zisserman, 2014), VGG19 (Wen et al, 2019), MobileNet V2 (Sandler et al, 2018), Xception (Chollet, 2017), Inception V2 (Alamsyah and Fachrurrozi, 2019), Inception-Resnet-V2 (Szegedy et al, 2017) and more.…”

Section: The Proposed Hybrid DL Approach For Classification Step 221 the Transfer Learning Techniquementioning

confidence: 99%

Towards a Reliable and Sensitive Deep Learning Based Approach for Multi-Ship Detection in SAR Imagery

Elshafey

2021

IJG

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Synthetic Aperture Radar (SAR) images show promising results in monitoring maritime activities. Recently, Deep learning-based object detection techniques have impressive results in most detection applications but unfortunately there are challenging problems such as difficulty of detecting multiple ships, especially inshore ones. In this paper, a three-step ship detection process is described and a reliable and sensitive hybrid deep learning model is proposed as an efficient classifier in the middle step. The proposed model combines the finetuned Inception-Resnet-V2 model and the Long Short Term Memory model in two different approaches: parallel approach and cascaded approach. In experiments, the region proposal algorithm and the Non-Maxima suppression algorithm are applied in the first and last step in the three-step detection process. The comparative results show that the proposed approach in cascaded form outperforms the competitive recent state-of-the-art approaches by enhancement up to 16.3%, 16.5%, and 18.9% in terms of recall, precision and mean average precision, respectively. Moreover, the proposed approach shows high relative sensitivity for challenged cases of both inshore and offshore scenes by enhancement ratios up to 81.88% and 24.58%, respectively in recall perspective.

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A New Transfer Learning Based on VGG-19 Network for Fault Diagnosis

Cited by 128 publications

References 14 publications

Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network

Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network

Convolutional Neural Network Based Ensemble Approach for Homoglyph Recognition

Towards a Reliable and Sensitive Deep Learning Based Approach for Multi-Ship Detection in SAR Imagery

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