scholarly journals Multi-Scale Convolutional Recurrent Neural Network for Bearing Fault Detection in Noisy Manufacturing Environments

2021 ◽  
Vol 11 (9) ◽  
pp. 3963
Author(s):  
Seokju Oh ◽  
Seugmin Han ◽  
Jongpil Jeong
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Random Noise ◽  
Multi Scale ◽  
Bearing Defects ◽  
Cause Of Failure ◽  
Equipment Failures ◽  
Rotating Equipment ◽  
Bearing Damage ◽  
Manufacturing Environments

The failure of a facility to produce a product can have significant impacts on the quality of the product. Most equipment failures occur in rotating equipment, with bearing damage being the biggest cause of failure in rotating equipment. In this paper, we propose a denoising autoencoder (DAE) and multi-scale convolution recurrent neural network (MS-CRNN), wherein the DAE accurately inspects bearing defects in the same environment as bearing vibration signals in the field, and the MS-CRNN inspects and classifies defects. We experimented with adding random noise to create a dataset that resembled noisy manufacturing installations in the field. From the results of the experiment, the accuracy of the proposed method was more than 90%, proving that it is an algorithm that can be applied in the field.

Automatic Segmentation and Classification of COVID-19 CT Image Using Deep Learning and Multi-Scale Recurrent Neural Network Based Classifier

2021 ◽  
Vol 11 (10) ◽  
pp. 2618-2625
Author(s):  
R. T. Subhalakshmi ◽  
S. Appavu Alias Balamurugan ◽  
S. Sasikala
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Superior Performance ◽  
Multi Scale ◽  
Proposed Model ◽  
Class Labels

In recent times, the COVID-19 epidemic turn out to be increased in an extreme manner, by the accessibility of an inadequate amount of rapid testing kits. Consequently, it is essential to develop the automated techniques for Covid-19 detection to recognize the existence of disease from the radiological images. The most ordinary symptoms of COVID-19 are sore throat, fever, and dry cough. Symptoms are able to progress to a rigorous type of pneumonia with serious impediment. As medical imaging is not recommended currently in Canada for crucial COVID-19 diagnosis, systems of computer-aided diagnosis might aid in early COVID-19 abnormalities detection and help out to observe the disease progression, reduce mortality rates potentially. In this approach, a deep learning based design for feature extraction and classification is employed for automatic COVID-19 diagnosis from computed tomography (CT) images. The proposed model operates on three main processes based pre-processing, feature extraction, and classification. The proposed design incorporates the fusion of deep features using GoogLe Net models. Finally, Multi-scale Recurrent Neural network (RNN) based classifier is applied for identifying and classifying the test CT images into distinct class labels. The experimental validation of the proposed model takes place using open-source COVID-CT dataset, which comprises a total of 760 CT images. The experimental outcome defined the superior performance with the maximum sensitivity, specificity, and accuracy.

scholarly journals Motor Fault Detection Using Wavelet Transform and Improved PSO-BP Neural Network

Processes ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1322 ◽  
Author(s):  
Chun-Yao Lee ◽  
Yi-Hsin Cheng
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Fault Detection ◽  
Bp Neural Network ◽  
Short Circuit ◽  
Back Propagation ◽  
Operating Environment ◽  
Cause Of Failure ◽  
Bearing Damage ◽  
Improved Pso

This paper proposes a motor fault detection method based on wavelet transform (WT) and improved PSO-BP neural network which is combined with improved particle swarm optimization (PSO) and a back propagation (BP) neural network with linearly increasing inertia weight. First, this research used WT to analyze the current signals of the healthy motor, bearing damage motor, stator winding inter-turn short circuit motor, and broken rotor bar motor. Second, features after completing the signal analysis were extracted, and three types of classifiers were used to classify. The results show that the improved PSO-BP neural network can effectively detect the cause of failure. In addition, in order to simulate the actual operating environment of the motor, this study added white noise with signal noise ratios of 30 dB, 25 dB, and 20 dB to verify that this model has a better anti-noise ability.

Multi-scale attention guided recurrent neural network for deformation map forecasting

2021 ◽  
Author(s):  
Ram Prabhakar Kathirvel ◽  
Veera Hari Krishna ◽  
Madhumitha Nayak ◽  
Jayavardhana Gubbi ◽  
Balamuralidhar Purushothaman
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Multi Scale ◽  
Deformation Map

scholarly journals Multi-scale Hierarchical Residual Network for Dense Captioning

2019 ◽  
Vol 64 ◽  
pp. 181-196 ◽  
Author(s):  
Yan Tian ◽  
Xun Wang ◽  
Jiachen Wu ◽  
Ruili Wang ◽  
Bailin Yang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Feature Space ◽  
Image Feature ◽  
Feature Maps ◽  
Residual Network ◽  
Multi Scale ◽  
Residual Learning ◽  
Novel Approach ◽  
Great Progress

Recent research on dense captioning based on the recurrent neural network and the convolutional neural network has made a great progress. However, mapping from an image feature space to a description space is a nonlinear and multimodel task, which makes it difficult for the current methods to get accurate results. In this paper, we put forward a novel approach for dense captioning based on hourglass-structured residual learning. Discriminant feature maps are obtained by incorporating dense connected networks and residual learning in our model. Finally, the performance of the approach on the Visual Genome V1.0 dataset and the region labelled MS-COCO (Microsoft Common Objects in Context) dataset are demonstrated. The experimental results have shown that our approach outperforms most current methods.

scholarly journals Multi-scale Convolutional Recurrent Neural Network and Data Augmentation for Polyphonic Sound Event Detection

2021 ◽  
Vol 1769 (1) ◽  
pp. 012008
Author(s):  
Keming Zhang ◽  
Yuanwen Cai ◽  
Yuan Ren ◽  
Ruida Ye ◽  
Xianwei Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Event Detection ◽  
Data Augmentation ◽  
Multi Scale ◽  
Sound Event ◽  
Sound Event Detection
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