scholarly journals Bearing Defect Detection with Unsupervised Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jianqiao Xu ◽  
Zhaolu Zuo ◽  
Danchao Wu ◽  
Bing Li ◽  
Xiaoni Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Defect Detection ◽  
Surface Defects ◽  
Bearing Defects ◽  
Black Spots ◽  
Bearing Defect ◽  
Unsupervised Neural Network ◽  
Actual Use ◽  
Unsupervised Neural Networks

Bearings always suffer from surface defects, such as scratches, black spots, and pits. Those surface defects have great effects on the quality and service life of bearings. Therefore, the defect detection of the bearing has always been the focus of the bearing quality control. Deep learning has been successfully applied to the objection detection due to its excellent performance. However, it is difficult to realize automatic detection of bearing surface defects based on data-driven-based deep learning due to few samples data of bearing defects on the actual production line. Sample preprocessing algorithm based on normalized sample symmetry of bearing is adopted to greatly increase the number of samples. Two different convolutional neural networks, supervised networks and unsupervised networks, are tested separately for the bearing defect detection. The first experiment adopts the supervised networks, and ResNet neural networks are selected as the supervised networks in this experiment. The experiment result shows that the AUC of the model is 0.8567, which is low for the actual use. Also, the positive and negative samples should be labelled manually. To improve the AUC of the model and the flexibility of the samples labelling, a new unsupervised neural network based on autoencoder networks is proposed. Gradients of the unlabeled data are used as labels, and autoencoder networks are created with U-net to predict the output. In the second experiment, positive samples of the supervised experiment are used as the training set. The experiment of the unsupervised neural networks shows that the AUC of the model is 0.9721. In this experiment, the AUC is higher than the first experiment, but the positive samples must be selected. To overcome this shortage, the dataset of the third experiment is the same as the supervised experiment, where all the positive and negative samples are mixed together, which means that there is no need to label the samples. This experiment shows that the AUC of the model is 0.9623. Although the AUC is slightly lower than that of the second experiment, the AUC is high enough for actual use. The experiment results demonstrate the feasibility and superiority of the proposed unsupervised networks.

scholarly journals Plastic Gasket Defect Detection Based on Transfer Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xieyi Chen ◽  
Dongyun Wang ◽  
Jinjun Shao ◽  
Jun Fan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Defect Detection ◽  
Data Augmentation ◽  
Surface Defects ◽  
Visual Detection ◽  
Training Data

To automatically detect plastic gasket defects, a set of plastic gasket defect visual detection devices based on GoogLeNet Inception-V2 transfer learning was designed and established in this study. The GoogLeNet Inception-V2 deep convolutional neural network (DCNN) was adopted to extract and classify the defect features of plastic gaskets to solve the problem of their numerous surface defects and difficulty in extracting and classifying the features. Deep learning applications require a large amount of training data to avoid model overfitting, but there are few datasets of plastic gasket defects. To address this issue, data augmentation was applied to our dataset. Finally, the performance of the three convolutional neural networks was comprehensively compared. The results showed that the GoogLeNet Inception-V2 transfer learning model had a better performance in less time. It means it had higher accuracy, reliability, and efficiency on the dataset used in this paper.

scholarly journals Deep Learning Convolutional Neural Networks for Pharmaceutical Tablet Defect Detection

2020 ◽  
Vol 26 (S2) ◽  
pp. 1606-1609
Author(s):  
Xiangyu Ma ◽  
Nada Kittikunakorn ◽  
Bradley Sorman ◽  
Hanmi Xi ◽  
Antong Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Defect Detection ◽  
Pharmaceutical Tablet

Effect of the Denoising on Acoustic Emission Signals

2010 ◽  
Vol 132 (3) ◽  
Author(s):  
X. Chiementin ◽  
D. Mba ◽  
B. Charnley ◽  
S. Lignon ◽  
J. P. Dron
Keyword(s):  
Acoustic Emission ◽  
Defect Detection ◽  
Vibration Analysis ◽  
The Self ◽  
Adaptive Noise Cancellation ◽  
Test Rig ◽  
Bearing Defects ◽  
Bearing Defect ◽  
Adaptive Noise ◽  
Statistical Indicators

The acoustic emission (AE) technology is growing in its applicability to bearing defect diagnosis. Several publications have shown its effectiveness for earlier detection of bearing defects than vibration analysis. In the latter instance, detection and monitoring of defects can be achieved through temporal statistical indicators, which can further be improved by application of denoising techniques. This paper investigates the application of temporal statistical indicators for AE detection of bearing defects on a purposely built test-rig and assesses the effectiveness of various denoising techniques in improving sensitivity to early defect detection. It is concluded that the denoising methods offer significant improvements in identifying defects with AE, especially the self-adaptive noise cancellation method.

Image-based Surface Defect Detection Using Deep Learning: A Review

2021 ◽  
pp. 1-23
Author(s):  
Prahar Bhatt ◽  
Rishi K. Malhan ◽  
Pradeep Rajendran ◽  
Brual Shah ◽  
Shantanu Thakar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Surface Defects ◽  
Future Research ◽  
Specific Class ◽  
Survey Paper ◽  
Context Learning ◽  
Surface Defect Detection ◽  
Manufacturing Applications ◽  
Processing Techniques

Abstract Automatically detecting surface defects from images is an essential capability in manufacturing applications. Traditional image processing techniques were useful in solving a specific class of problems. However, these techniques were unable to handle noise, variations in lighting conditions, and background with complex textures. Increasingly deep learning is being explored to automate defect detection. This survey paper presents three different ways of classifying various efforts. These are based on defect detection context, learning techniques, and defect localization and classification method. The existing literature is classified using this methodology. The paper also identifies future research directions based on the trends in the deep learning area.

scholarly journals An Autoencoder-Based Deep Learning Classifier for Efficient Diagnosis of Autism

Children ◽  
2020 ◽  
Vol 7 (10) ◽  
pp. 182
Author(s):  
Harshini Sewani ◽  
Rasha Kashef
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Data Exchange ◽  
Neurodevelopmental Disorder ◽  
Autism Spectrum ◽  
Imaging Data ◽  
Neural Network Learning ◽  
Network Learning ◽  
Unsupervised Neural Network

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by a lack of social communication and social interaction. Autism is a mental disorder investigated by social and computational intelligence scientists utilizing advanced technologies such as machine learning models to enhance clinicians’ ability to provide robust diagnosis and prognosis of autism. However, with dynamic changes in autism behaviour patterns, these models’ quality and accuracy have become a great challenge for clinical practitioners. We applied a deep neural network learning on a large brain image dataset obtained from ABIDE (autism brain imaging data exchange) to provide an efficient diagnosis of ASD, especially for children. Our deep learning model combines unsupervised neural network learning, an autoencoder, and supervised deep learning using convolutional neural networks. Our proposed algorithm outperforms individual-based classifiers measured by various validations and assessment measures. Experimental results indicate that the autoencoder combined with the convolution neural networks provides the best performance by achieving 84.05% accuracy and Area under the Curve (AUC) value of 0.78.

Application of Deep Learning Convolutional Neural Networks for Internal Tablet Defect Detection: High Accuracy, Throughput, and Adaptability

2020 ◽  
Vol 109 (4) ◽  
pp. 1547-1557 ◽  
Author(s):  
Xiangyu Ma ◽  
Nada Kittikunakorn ◽  
Bradley Sorman ◽  
Hanmi Xi ◽  
Antong Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Defect Detection ◽  
High Accuracy

Automatic image detection of multi-type surface defects on wind turbine blades based on cascade deep learning network

2021 ◽  
Vol 25 (2) ◽  
pp. 463-482
Author(s):  
Yulin Mao ◽  
Shuangxin Wang ◽  
Dingli Yu ◽  
Juchao Zhao
Keyword(s):  
Deep Learning ◽  
Wind Turbine ◽  
Defect Detection ◽  
Surface Defects ◽  
Oil Pollution ◽  
Turbine Blades ◽  
Critical Factor ◽  
Wind Turbine Blades ◽  
Surface Defect Detection ◽  
Deep Learning Network

A safe operation protocol of the wind blades is a critical factor to ensure the stability of a wind turbine. Sensors are most commonly applied for defect detection on wind turbine blades (WTBs). However, due to the high cost and the sensitivity to stochastic noise, computer vision-guided automatic detection remains a challenge for surface defect detection on WTBs in particularly, its accuracy in locating defects is yet to be optimized. In this paper, we developed a visual inspection model that can automatically and precisely classify and locate the surface defects, through the utilization of a deep learning framework based on the Cascade R-CNN. In order to obtain high mean average precision (mAP) according to the characteristics of the dataset, a model named Contextual Aligned-Deformable Cascade R-CNN (CAD Cascade R-CNN) using improved strategies of transfer learning, Deformable Convolution and Deformable RoI Align, as well as context information fusion is proposed and a dataset with surface defects categorized and labeled as crack, breakage and oil pollution is generated. Moreover to alleviate the problem of false detection under a complex background, an improved bisecting k-means is presented during the test process. The adaptability and generalization of the proposed CAD Cascade R-CNN model were validated by each type of defects in dataset and different IoU thresholds, whereas, each of the above improved strategies was verified by gradual ablation experiments. Finally experiments that compared with the baseline Cascade R-CNN, Faster R-CNN and YOLO-v3 demonstrate its superiority over these existing approaches with a maximum of 92.1% mAP.

scholarly journals Synthetic Data Generation for Steel Defect Detection and Classification Using Deep Learning

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1176
Author(s):  
Aleksei Boikov ◽  
Vladimir Payor ◽  
Roman Savelev ◽  
Alexandr Kolesnikov
Keyword(s):  
Neural Networks ◽  
Defect Detection ◽  
Production Control ◽  
Surface Defects ◽  
Synthetic Data ◽  
Real Data ◽  
Data Generation ◽  
Synthetic Data Generation ◽  
Steel Slab ◽  
Defect Recognition

The paper presents a methodology for training neural networks for vision tasks on synthesized data on the example of steel defect recognition in automated production control systems. The article describes the process of dataset procedural generation of steel slab defects with a symmetrical distribution. The results of training two neural networks Unet and Xception on a generated data grid and testing them on real data are presented. The performance of these neural networks was assessed using real data from the Severstal: Steel Defect Detection set. In both cases, the neural networks showed good results in the classification and segmentation of surface defects of steel workpieces in the image. Dice score on synthetic data reaches 0.62, and accuracy—0.81.

scholarly journals Automating Visual Inspection with Convolutional Neural Networks

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Sreerupa Das ◽  
Christopher D Hollander ◽  
Suraiya Suliman
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Defect Detection ◽  
Surface Defects ◽  
Training Data ◽  
Subject Matter Experts ◽  
Data Set ◽  
Analysis Task ◽  
Aerial Vehicle ◽  
Initial Results

Convolutional Neural Networks (CNNs) have become the recent tool of choice for many visual detection tasks, including object classification, localization, detection, and segmentation. CNNs are specialized neural networks composed of many layers and specifically designed to analyze grid-like data, e.g. images. One of the key features of a CNN is its ability to automatically detect important features within an image (e.g. edges, patterns, shapes); prior to CNNs, these features had to be manually engineered by subject matter experts. Inspired by the significant achievements and success that CNNs have experienced in the domain of computer vision, we examine a specific convolutional neural network (CNN) architecture, U-Net, suited for the task of visual defect detection. We identify and discuss situations for the use of this architecture in the specific context of external defect detection on aircraft and experimentally discuss its performance across a dataset of common visual defects. One requirement of training Convolution Networks on an image analysis task is the need for a large image (training) data set.  We address this problem by using synthetically generated images from computer models of jets with varying angles and perspectives with and without induced faults in the generated images.  This paper presents the initial results of using CNNs, specifically U-Net, to detect aerial vehicle surface defects of three categories.  We further demonstrate that CNNs trained on synthetic images can then be used to detect faults in real images of jets with visual damages.  The results obtained in this research, indicate that our approach has been quite effective in detecting surface anomalies in our tests.

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