Application of deep neural network and generative adversarial network to industrial maintenance: A case study of induction motor fault detection

2017 ◽  
Author(s):  
Yong Oh Lee ◽  
Jun Jo ◽  
Jongwoon Hwang
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Induction Motor ◽  
Deep Neural Network ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Industrial Maintenance

scholarly journals SYNTHETIC THERMAL BACKGROUND AND OBJECT TEXTURE GENERATION USING GEOMETRIC INFORMATION AND GAN

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 149-154
Author(s):  
V. A. Mizginov ◽  
S. Y. Danilov
Keyword(s):  
Neural Network ◽  
3D Modeling ◽  
Deep Neural Network ◽  
Infrared Image ◽  
Computation Time ◽  
Image Synthesis ◽  
3D Models ◽  
Geometric Information ◽  
Generative Adversarial Network ◽  
Adversarial Network

<p><strong>Abstract.</strong> Nowadays methods based on deep neural networks show the best performance among image recognition and object detection algorithms. Nevertheless, such methods require to have large databases of multispectral images of various objects to achieve state-of-the-art results. Therefore the dataset generation is one of the major challenges for the successful training of a deep neural network. However, infrared image datasets that are large enough for successful training of a deep neural network are not available in the public domain. Generation of synthetic datasets using 3D models of various scenes is a time-consuming method that requires long computation time and is not very realistic. This paper is focused on the development of the method for thermal image synthesis using a GAN (generative adversarial network). The aim of the presented work is to expand and complement the existing datasets of real thermal images. Today, deep convolutional networks are increasingly used for the goal of synthesizing various images. Recently a new generation of such algorithms commonly called GAN has become a promising tool for synthesizing images of various spectral ranges. These networks show effective results for image-to-image translations. While it is possible to generate a thermal texture for a single object, generation of environment textures is extremely difficult due to the presence of a large number of objects with different emission sources. The proposed method is based on a joint approach that uses 3D modeling and deep learning. Synthesis of background textures and objects textures is performed using a generative-adversarial neural network and semantic and geometric information about objects generated using 3D modeling. The developed approach significantly improves the realism of the synthetic images, especially in terms of the quality of background textures.</p>

scholarly journals An Automobile Environment Detection System Based on Deep Neural Network and its Implementation Using IoT-Enabled In-Vehicle Air Quality Sensors

2020 ◽  
Vol 12 (6) ◽  
pp. 2475 ◽  
Author(s):  
Jae-joon Chung ◽  
Hyun-Jung Kim
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Air Quality ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Detection System ◽  
Learning Models ◽  
Generative Adversarial Network ◽  
Detection Model ◽  
Adversarial Network

This paper elucidates the development of a deep learning–based driver assistant that can prevent driving accidents arising from drowsiness. As a precursor to this assistant, the relationship between the sensation of sleep depravity among drivers during long journeys and CO2 concentrations in vehicles is established. Multimodal signals are collected by the assistant using five sensors that measure the levels of CO, CO2, and particulate matter (PM), as well as the temperature and humidity. These signals are then transmitted to a server via the Internet of Things, and a deep neural network utilizes this information to analyze the air quality in the vehicle. The deep network employs long short-term memory (LSTM), skip-generative adversarial network (GAN), and variational auto-encoder (VAE) models to build an air quality anomaly detection model. The deep learning models gather data via LSTM, while the semi-supervised deep learning models collect data via GANs and VAEs. The purpose of this assistant is to provide vehicle air quality information, such as PM alerts and sleep-deprived driving alerts, to drivers in real time and thereby prevent accidents.

scholarly journals Data Augmentation Using Generative Adversarial Network for Automatic Machine Fault Detection Based on Vibration Signals

2021 ◽  
Vol 11 (5) ◽  
pp. 2166
Author(s):  
Van Bui ◽  
Tung Lam Pham ◽  
Huy Nguyen ◽  
Yeong Min Jang
Keyword(s):  
Fault Detection ◽  
Data Augmentation ◽  
Model Performance ◽  
Original Data ◽  
Fault Classification ◽  
Training Process ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Adversarial Network ◽  
Machine Fault

In the last decade, predictive maintenance has attracted a lot of attention in industrial factories because of its wide use of the Internet of Things and artificial intelligence algorithms for data management. However, in the early phases where the abnormal and faulty machines rarely appeared in factories, there were limited sets of machine fault samples. With limited fault samples, it is difficult to perform a training process for fault classification due to the imbalance of input data. Therefore, data augmentation was required to increase the accuracy of the learning model. However, there were limited methods to generate and evaluate the data applied for data analysis. In this paper, we introduce a method of using the generative adversarial network as the fault signal augmentation method to enrich the dataset. The enhanced data set could increase the accuracy of the machine fault detection model in the training process. We also performed fault detection using a variety of preprocessing approaches and classified the models to evaluate the similarities between the generated data and authentic data. The generated fault data has high similarity with the original data and it significantly improves the accuracy of the model. The accuracy of fault machine detection reaches 99.41% with 20% original fault machine data set and 93.1% with 0% original fault machine data set (only use generate data only). Based on this, we concluded that the generated data could be used to mix with original data and improve the model performance.

Series Arc Fault Detection Based on Random Forest and Deep Neural Network

2021 ◽  
pp. 1-1
Author(s):  
Jun Jiang ◽  
Wei Li ◽  
Zhe Wen ◽  
Yifan Bie ◽  
Harald Schwarz ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Fault Detection ◽  
Deep Neural Network

AN AI-BASED APPROACH TO ENHANCED FRACTURE RESOLUTION IN IMAGE LOGS

2021 ◽  
Author(s):  
James Howard ◽  
◽  
Joe Tracey ◽  
Mike Shen ◽  
Shawn Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Nearest Neighbor ◽  
Rock Fracture ◽  
Short Interval ◽  
Acoustic Properties ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Deep Learning Neural Network ◽  
Borehole Image

Borehole image logs are used to identify the presence and orientation of fractures, both natural and induced, found in reservoir intervals. The contrast in electrical or acoustic properties of the rock matrix and fluid-filled fractures is sufficiently large enough that sub-resolution features can be detected by these image logging tools. The resolution of these image logs is based on the design and operation of the tools, and generally is in the millimeter per pixel range. Hence the quantitative measurement of actual width remains problematic. An artificial intelligence (AI) -based workflow combines the statistical information obtained from a Machine-Learning (ML) segmentation process with a multiple-layer neural network that defines a Deep Learning process that enhances fractures in a borehole image. These new images allow for a more robust analysis of fracture widths, especially those that are sub-resolution. The images from a BHTV log were first segmented into rock and fluid-filled fractures using a ML-segmentation tool that applied multiple image processing filters that captured information to describe patterns in fracture-rock distribution based on nearest-neighbor behavior. The robust ML analysis was trained by users to identify these two components over a short interval in the well, and then the regression model-based coefficients applied to the remaining log. Based on the training, each pixel was assigned a probability value between 1.0 (being a fracture) and 0.0 (pure rock), with most of the pixels assigned one of these two values. Intermediate probabilities represented pixels on the edge of rock-fracture interface or the presence of one or more sub-resolution fractures within the rock. The probability matrix produced a map or image of the distribution of probabilities that determined whether a given pixel in the image was a fracture or partially filled with a fracture. The Deep Learning neural network was based on a Conditional Generative Adversarial Network (cGAN) approach where the probability map was first encoded and combined with a noise vector that acted as a seed for diverse feature generation. This combination was used to generate new images that represented the BHTV response. The second layer of the neural network, the adversarial or discriminator portion, determined whether the generated images were representative of the actual BHTV by comparing the generated images with actual images from the log and producing an output probability of whether it was real or fake. This probability was then used to train the generator and discriminator models that were then applied to the entire log. Several scenarios were run with different probability maps. The enhanced BHTV images brought out fractures observed in the core photos that were less obvious in the original BTHV log through enhanced continuity and improved resolution on fracture widths.

scholarly journals Convolutional Neural Network Audio Classifier for Alarm Sound Detection

2019 ◽  
Vol 8 (6) ◽  
pp. 4554-4557
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Sound Recognition ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Differential Network ◽  
Sound Detection ◽  
Long Short Term Memory ◽  
Lstm Network

Neural Networks (ANN) has evolved through many stages in the last three decades with many researchers contributing in this challenging field. With the power of math complex problems can also be solved by ANNs. ANNs like Convolutional Neural Network (CNN), Deep Neural network, Generative Adversarial Network (GAN), Long Short Term Memory (LSTM) network, Recurrent Neural Network (RNN), Ordinary Differential Network etc., are playing promising roles in many MNCs and IT industries for their predictions and accuracy. In this paper, Convolutional Neural Network is used for prediction of Beep sounds in high noise levels. Based on Supervised Learning, the research is developed the best CNN architecture for Beep sound recognition in noisy situations. The proposed method gives better results with an accuracy of 96%. The prototype is tested with few architectures for the training and test data out of which a two layer CNN classifier predictions were the best.

Sign in / Sign up

Export Citation Format

Share Document