scholarly journals A Generalizable Sample Resolution Augmentation Method for Mechanical Fault Diagnosis Based on ESPCN

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhenyun Chu ◽  
Shanshan Ji ◽  
Jinrui Wang ◽  
Xiaoyu Wang ◽  
Zongzhen Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
High Resolution ◽  
Data Augmentation ◽  
Simple Algorithm ◽  
Fault Classification ◽  
Training Dataset ◽  
Low Resolution ◽  
Stacked Autoencoders ◽  
Set Up ◽  
Resolution Dataset

Data augmentation has become a hot topic in the field of mechanical intelligent fault diagnosis. It can expand the limited training dataset by generating simulated samples, but there is still no effective method augmenting the resolution of low resolution sample. In this paper, a simple algorithm, namely, efficient subpixel convolutional neural network (ESPCN), is proposed to solve this deficiency. The ESPCN model performs the arrange operation on the raw low resolution data through the subpixel layer and outputs the result of four-channel multifeature maps. Then, the sample resolution is increased to four times compared with the raw low resolution sample. Finally, the generated high resolution dataset is employed to train the stacked autoencoders (SAE) for fault classification, and the raw high resolution dataset is used for testing. Two fault diagnosis cases with different sample dimensions and rotating speeds are set up to simulate the low resolution situation, and the experimental results verify the feasibility of the proposed algorithm.

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 The Effect of Visitors on Zoo Reptile Behaviour during the COVID-19 Pandemic

2021 ◽  
Vol 2 (4) ◽  
pp. 664-676
Author(s):  
Kimberley C. Carter ◽  
Isabel A. T. Keane ◽  
Lisa M. Clifforde ◽  
Lewis J. Rowden ◽  
Léa Fieschi-Méric ◽  
...  
Keyword(s):  
High Resolution ◽  
Species Selection ◽  
Low Resolution ◽  
Gekko Gecko ◽  
Video Footage ◽  
Reptile Species ◽  
Zoo Animals ◽  
Species Specific ◽  
The Uk ◽  
Resolution Dataset

Visitors to zoos can have positive, neutral, or negative relationships with zoo animals. This makes human–animal interactions (HAIs) an essential component of welfare and an important consideration in species selection for zoo exhibits and in enclosure designs. We measured the effect of visitors on reptiles by comparing open and closed periods during the lockdowns in response to the COVID-19 pandemic in the UK in a low-resolution dataset for thirteen species of reptiles and a high-resolution dataset focussing on just one of these. Scan sampling on thirteen reptile species (two chelonians and eleven squamates) showed species-specific differences in response to the presence/absence of visitors, with most taxa being only weakly affected. High-resolution scan sampling via video footage of an off-show and on-show enclosure was carried out for tokay geckos (Gekko gecko) over the open and closed periods. In this part of the study, tokay geckos were significantly more visible during zoo closure than when visitors were present on-exhibit, but there was no change in off-show animals, indicating the effect of visitors as opposed to other factors, such as seasonality, which applied equally to both on- and off-show animals. The high-resolution study showed that a significant effect was present for tokay geckos, even though the low-resolution suggested that they were more weakly affected than other taxa. Our results indicate that, for cryptic species such as this, more intensive sampling may be required to properly understand visitor effects. Our data do not allow the interpretation of effects on welfare but show that such assessments require a species-specific approach.

Multi-Fault Classifier Based on Support Vector Machine and Its Application

2005 ◽  
Vol 293-294 ◽  
pp. 483-492 ◽  
Author(s):  
Zhou Suo Zhang ◽  
Minghui Shen ◽  
Wenzhi Lv ◽  
Zheng Jia He
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Steam Turbine ◽  
Time Domain ◽  
Simple Algorithm ◽  
Fault Classification ◽  
Support Vector ◽  
Turbine Generator ◽  
Signal Features ◽  
Multi Classification

Aiming at problem on limiting development of machinery fault intelligent diagnosis due to needing many fault data samples, this paper improves a multi-classification algorithm of support vector machine, and a multi-fault classifier based on the algorithm is constructed. Training the multi-fault classifier only needs a small quantity of fault data samples in time domain, and does not need signal preprocessing of extracting signal features. The multi-fault classifier has been applied to fault diagnosis of steam turbine generator, and the results show that it has such simple algorithm, online fault classification and excellent capability of fault classification as advantages.

scholarly journals Can the Structure Similarity of Training Patches Affect the Sea Surface Temperature Deep Learning Super-Resolution?

2021 ◽  
Vol 13 (18) ◽  
pp. 3568
Author(s):  
Bo Ping ◽  
Yunshan Meng ◽  
Cunjin Xue ◽  
Fenzhen Su
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Image Quality ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Super Resolution ◽  
Sea Surface ◽  
Training Dataset ◽  
Low Resolution ◽  
Structure Similarity

Meso- and fine-scale sea surface temperature (SST) is an essential parameter in oceanographic research. Remote sensing is an efficient way to acquire global SST. However, single infrared-based and microwave-based satellite-derived SST cannot obtain complete coverage and high-resolution SST simultaneously. Deep learning super-resolution (SR) techniques have exhibited the ability to enhance spatial resolution, offering the potential to reconstruct the details of SST fields. Current SR research focuses mainly on improving the structure of the SR model instead of training dataset selection. Different from generating the low-resolution images by downscaling the corresponding high-resolution images, the high- and low-resolution SST are derived from different sensors. Hence, the structure similarity of training patches may affect the SR model training and, consequently, the SST reconstruction. In this study, we first discuss the influence of training dataset selection on SST SR performance, showing that the training dataset determined by the structure similarity index (SSIM) of 0.6 can result in higher reconstruction accuracy and better image quality. In addition, in the practical stage, the spatial similarity between the low-resolution input and the objective high-resolution output is a key factor for SST SR. Moreover, the training dataset obtained from the actual AMSR2 and MODIS SST images is more suitable for SST SR because of the skin and sub-skin temperature difference. Finally, the SST reconstruction accuracies obtained from different SR models are relatively consistent, yet the differences in reconstructed image quality are rather significant.

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.

Low-Loss Images in a Stem/Tem Microscope

1976 ◽  
Vol 34 ◽  
pp. 496-497 ◽  
Author(s):  
David C. Joy ◽  
Dennis M. Maher
Keyword(s):  
High Resolution ◽  
Surface Topography ◽  
Beam Direction ◽  
Low Loss ◽  
Specimen Holder ◽  
Glancing Angle ◽  
High Resolution Images ◽  
Set Up ◽  
Conventional Manner ◽  
Objective Type

High-resolution images of the surface topography of solid specimens can be obtained using the low-loss technique of Wells. If the specimen is placed inside a lens of the condenser/objective type, then it has been shown that the lens itself can be used to collect and filter the low-loss electrons. Since the probeforming lenses in TEM instruments fitted with scanning attachments are of this type, low-loss imaging should be possible.High-resolution, low-loss images have been obtained in a JEOL JEM 100B fitted with a scanning attachment and a thermal, fieldemission gun. No modifications were made to the instrument, but a wedge-shaped, specimen holder was made to fit the side-entry, goniometer stage. Thus the specimen is oriented initially at a glancing angle of about 30° to the beam direction. The instrument is set up in the conventional manner for STEM operation with all the lenses, including the projector, excited.

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.

MODELING THE SOUTH ATLANTIC OCEAN FROM MEDIUM TO HIGH RESOLUTION

2014 ◽  
Vol 31 (2) ◽  
Author(s):  
Mariela Gabioux ◽  
Vladimir Santos da Costa ◽  
Joao Marcos Azevedo Correia de Souza ◽  
Bruna Faria de Oliveira ◽  
Afonso De Moraes Paiva
Keyword(s):  
High Resolution ◽  
Atlantic Ocean ◽  
Large Scale ◽  
South Atlantic ◽  
Surface Relaxation ◽  
The South ◽  
Small Surface ◽  
Basic Model ◽  
Basic Set ◽  
Set Up

Results of the basic model configuration of the REMO project, a Brazilian approach towards operational oceanography, are discussed. This configuration consists basically of a high-resolution eddy-resolving, 1/12 degree model for the Metarea V, nested in a medium-resolution eddy-permitting, 1/4 degree model of the Atlantic Ocean. These simulations performed with HYCOM model, aim for: a) creating a basic set-up for implementation of assimilation techniques leading to ocean prediction; b) the development of hydrodynamics bases for environmental studies; c) providing boundary conditions for regional domains with increased resolution. The 1/4 degree simulation was able to simulate realistic equatorial and south Atlantic large scale circulation, both the wind-driven and the thermohaline components. The high resolution simulation was able to generate mesoscale and represent well the variability pattern within the Metarea V domain. The BC mean transport values were well represented in the southwestern region (between Vitória-Trinidade sea mount and 29S), in contrast to higher latitudes (higher than 30S) where it was slightly underestimated. Important issues for the simulation of the South Atlantic with high resolution are discussed, like the ideal place for boundaries, improvements in the bathymetric representation and the control of bias SST, by the introducing of a small surface relaxation. In order to make a preliminary assessment of the model behavior when submitted to data assimilation, the Cooper & Haines (1996) method was used to extrapolate SSH anomalies fields to deeper layers every 7 days, with encouraging results.

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