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.

scholarly journals Bearing Fault Diagnosis Based on the Switchable Normalization SSGAN with 1-D Representation of Vibration Signals as Input

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2000 ◽  
Author(s):  
Dongdong Zhao ◽  
Feng Liu ◽  
He Meng
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Fault Classification ◽  
Generative Adversarial Networks ◽  
Data Set ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Training Samples ◽  
Public Data

The bearing is a component of the support shaft that guides the rotational movement of the shaft, widely used in the mechanical industry and also called a mechanical joint. In bearing fault diagnosis, the accuracy much depends on the feature extraction, which always needs a lot of training samples and classification in the commonly used methods. Neural networks are good at latent feature extraction and fault classification, however, they have problems with instability and over-fitting, and more labeled samples must be trained. Switchable normalization and semi-supervised learning are introduced to solve the above obstacles in this paper, which proposes a novel bearing fault diagnosis method based on switchable normalization semi-supervised generative adversarial networks (SN-SSGAN) with 1-dimensional representation of vibration signals as input. Experimental results showed that the proposed method has a desirable 99.93% classification accuracy in the case of less labeled data from the public data set of West Reserve University, which is better than the state-of-the-art methods.

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.

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.

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 A Novel Method for the Deblurring of Photogrammetric Images Using Conditional Generative Adversarial Networks

2020 ◽  
Vol 12 (16) ◽  
pp. 2586 ◽  
Author(s):  
Pawel Burdziakowski
Keyword(s):  
Point Clouds ◽  
Aerial Images ◽  
Generative Adversarial Networks ◽  
Camera Motion ◽  
Data Set ◽  
Adversarial Networks ◽  
Blur Kernel ◽  
Almost All ◽  
Blurred Images

The visual data acquisition from small unmanned aerial vehicles (UAVs) may encounter a situation in which blur appears on the images. Image blurring caused by camera motion during exposure significantly impacts the images interpretation quality and consequently the quality of photogrammetric products. On blurred images, it is difficult to visually locate ground control points, and the number of identified feature points decreases rapidly together with an increasing blur kernel. The nature of blur can be non-uniform, which makes it hard to forecast for traditional deblurring methods. Due to the above, the author of this publication concluded that the neural methods developed in recent years were able to eliminate blur on UAV images with an unpredictable or highly variable blur nature. In this research, a new, rapid method based on generative adversarial networks (GANs) was applied for deblurring. A data set for neural network training was developed based on real aerial images collected over the last few years. More than 20 full sets of photogrammetric products were developed, including point clouds, orthoimages and digital surface models. The sets were generated from both blurred and deblurred images using the presented method. The results presented in the publication show that the method for improving blurred photo quality significantly contributed to an improvement in the general quality of typical photogrammetric products. The geometric accuracy of the products generated from deblurred photos was maintained despite the rising blur kernel. The quality of textures and input photos was increased. This research proves that the developed method based on neural networks can be used for deblur, even in highly blurred images, and it significantly increases the final geometric quality of the photogrammetric products. In practical cases, it will be possible to implement an additional feature in the photogrammetric software, which will eliminate unwanted blur and allow one to use almost all blurred images in the modelling process.

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