scholarly journals Imbalanced Fault Classification of Bearing via Wasserstein Generative Adversarial Networks with Gradient Penalty

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Baokun Han ◽  
Sixiang Jia ◽  
Guifang Liu ◽  
Jinrui Wang
Keyword(s):  
Fault Diagnosis ◽  
Feature Learning ◽  
Extraction Process ◽  
Fault Classification ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Excellent Performance ◽  
Adversarial Networks ◽  
And Training

Recently, generative adversarial networks (GANs) are widely applied to increase the amounts of imbalanced input samples in fault diagnosis. However, the existing GAN-based methods have convergence difficulties and training instability, which affect the fault diagnosis efficiency. This paper develops a novel framework for imbalanced fault classification based on Wasserstein generative adversarial networks with gradient penalty (WGAN-GP), which interpolates randomly between the true and generated samples to ensure that the transition region between the true and false samples satisfies the Lipschitz constraint. The process of feature learning is visualized to show the feature extraction process of WGAN-GP. To verify the availability of the generated samples, a stacked autoencoder (SAE) is set to classify the enhanced dataset composed of the generated samples and original samples. Furthermore, the exhibition of the loss curve indicates that WGAN-GP has better convergence and faster training speed due to the introduction of the gradient penalty. Three bearing datasets are employed to verify the effectiveness of the developed framework, and the results show that the proposed framework has an excellent performance in mechanical fault diagnosis under the imbalanced training dataset.

scholarly journals Generalization of Deep Neural Networks for Imbalanced Fault Classification of Machinery Using Generative Adversarial Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 111168-111180 ◽  
Author(s):  
Jinrui Wang ◽  
Shunming Li ◽  
Baokun Han ◽  
Zenghui An ◽  
Huaiqian Bao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Fault Classification ◽  
Generative Adversarial Networks ◽  
Adversarial Networks

scholarly journals Exploiting Generative Adversarial Networks as an Oversampling Method for Fault Diagnosis of an Industrial Robotic Manipulator

2020 ◽  
Vol 10 (21) ◽  
pp. 7712
Author(s):  
Ziqiang Pu ◽  
Diego Cabrera ◽  
René-Vinicio Sánchez ◽  
Mariela Cerrada ◽  
Chuan Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Initial Conditions ◽  
Robotic Manipulator ◽  
Learning Curves ◽  
Fault Classification ◽  
Generative Adversarial Networks ◽  
Unbalanced Data ◽  
Data Set ◽  
Adversarial Networks ◽  
Test Of Hypotheses

Data-driven machine learning techniques play an important role in fault diagnosis, safety, and maintenance of the industrial robotic manipulator. However, these methods require data that, more often that not, are hard to obtain, especially data collected from fault condition states and, without enough and appropriated (balanced) data, no acceptable performance should be expected. Generative adversarial networks (GAN) are receiving a significant interest, especially in the image analysis field due to their outstanding generative capabilities. This paper investigates whether or not GAN can be used as an oversampling tool to compensate for an unbalanced data set in an industrial manipulator fault diagnosis task. A comprehensive empirical analysis is performed taking into account six different scenarios for mitigating the unbalanced data, including classical under and oversampling (SMOTE) methods. In all of these, a wavelet packet transform is used for feature generation while a random forest is used for fault classification. Aspects such as loss functions, learning curves, random input distributions, data shuffling, and initial conditions were also considered. A non-parametric statistical test of hypotheses reveals that all GAN based fault-diagnosis outperforms both under and oversampling classical methods while, within GAN based methods, an average accuracy difference as high as 1.68% can be achieved.

Data augment method for machine fault diagnosis using conditional generative adversarial networks

2020 ◽  
Vol 234 (12) ◽  
pp. 2719-2727 ◽  
Author(s):  
Jinrui Wang ◽  
Baokun Han ◽  
Huaiqian Bao ◽  
Mingyan Wang ◽  
Zhenyun Chu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Data Augmentation ◽  
Fault Classification ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Stacked Autoencoders ◽  
Model Training ◽  
Conditional Information

As a useful data augmentation technique, generative adversarial networks have been successfully applied in fault diagnosis field. But traditional generative adversarial networks can only generate one category fault signals in one time, which is time-consuming and costly. To overcome this weakness, we develop a novel fault diagnosis method which combines conditional generative adversarial networks and stacked autoencoders, and both of them are built by stacking one-dimensional full connection layers. First, conditional generative adversarial networks is used to generate artificial samples based on the frequency samples, and category labels are adopted as the conditional information to simultaneously generate different category signals. Meanwhile, spectrum normalization is added to the discriminator of conditional generative adversarial networks to enhance the model training. Then, the augmented training samples are transferred to stacked autoencoders for feature extraction and fault classification. Finally, two datasets of bearing and gearbox are employed to investigate the effectiveness of the proposed conditional generative adversarial network–stacked autoencoder method.

Fault Classification of Hydroelectric Generating Unit Based on Improved Evidence Theory

2014 ◽  
Vol 7 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Jiatang Cheng ◽  
Li Ai ◽  
Zhimei Duan ◽  
Yan Xiong
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Evidence Theory ◽  
Fault Classification ◽  
Fusion Method ◽  
Inconsistent Result ◽  
High Conflict

Aiming at the problem of the conventional vibration fault diagnosis technology with inconsistent result of a hydroelectric generating unit, an information fusion method was proposed based on the improved evidence theory. In this algorithm, the original evidence was amended by the credibility factor, and then the synthesis rule of standard evidence theory was utilized to carry out information fusion. The results show that the proposed method can obtain any definitive conclusion even if there is high conflict evidence in the synthesis evidence process, and may avoid the divergent phenomenon when the consistent evidence is fused, and is suitable for the fault classification of hydroelectric generating unit.

scholarly journals Application of Deep Learning in Fault Diagnosis of Rotating Machinery

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 919
Author(s):  
Wanlu Jiang ◽  
Chenyang Wang ◽  
Jiayun Zou ◽  
Shuqing Zhang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Generative Adversarial Networks ◽  
Extraction Ability ◽  
One Dimensional ◽  
Adversarial Networks ◽  
Diagnosis Model ◽  
The One

The field of mechanical fault diagnosis has entered the era of “big data”. However, existing diagnostic algorithms, relying on artificial feature extraction and expert knowledge are of poor extraction ability and lack self-adaptability in the mass data. In the fault diagnosis of rotating machinery, due to the accidental occurrence of equipment faults, the proportion of fault samples is small, the samples are imbalanced, and available data are scarce, which leads to the low accuracy rate of the intelligent diagnosis model trained to identify the equipment state. To solve the above problems, an end-to-end diagnosis model is first proposed, which is an intelligent fault diagnosis method based on one-dimensional convolutional neural network (1D-CNN). That is to say, the original vibration signal is directly input into the model for identification. After that, through combining the convolutional neural network with the generative adversarial networks, a data expansion method based on the one-dimensional deep convolutional generative adversarial networks (1D-DCGAN) is constructed to generate small sample size fault samples and construct the balanced data set. Meanwhile, in order to solve the problem that the network is difficult to optimize, gradient penalty and Wasserstein distance are introduced. Through the test of bearing database and hydraulic pump, it shows that the one-dimensional convolution operation has strong feature extraction ability for vibration signals. The proposed method is very accurate for fault diagnosis of the two kinds of equipment, and high-quality expansion of the original data can be achieved.

scholarly journals Improving Skin Lesion Analysis with Generative Adversarial Networks

2020 ◽  
Author(s):  
Alceu Bissoto ◽  
Sandra Avila
Keyword(s):  
Skin Lesion ◽  
State Of The Art ◽  
Synthetic Data ◽  
Clinical Information ◽  
Analysis Data ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Classification Models ◽  
Adversarial Networks ◽  
Lesion Analysis

Melanoma is the most lethal type of skin cancer. Early diagnosis is crucial to increase the survival rate of those patients due to the possibility of metastasis. Automated skin lesion analysis can play an essential role by reaching people that do not have access to a specialist. However, since deep learning became the state-of-the-art for skin lesion analysis, data became a decisive factor in pushing the solutions further. The core objective of this M.Sc. dissertation is to tackle the problems that arise by having limited datasets. In the first part, we use generative adversarial networks to generate synthetic data to augment our classification model’s training datasets to boost performance. Our method generates high-resolution clinically-meaningful skin lesion images, that when compound our classification model’s training dataset, consistently improved the performance in different scenarios, for distinct datasets. We also investigate how our classification models perceived the synthetic samples and how they can aid the model’s generalization. Finally, we investigate a problem that usually arises by having few, relatively small datasets that are thoroughly re-used in the literature: bias. For this, we designed experiments to study how our models’ use data, verifying how it exploits correct (based on medical algorithms), and spurious (based on artifacts introduced during image acquisition) correlations. Disturbingly, even in the absence of any clinical information regarding the lesion being diagnosed, our classification models presented much better performance than chance (even competing with specialists benchmarks), highly suggesting inflated performances.

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