Fault Detection in Soft-started Induction Motors using Convolutional Neural Network Enhanced by Data Augmentation Techniques

This paper presents an improved method for recognizing the drill state on the basis of hole images drilled in a laminated chipboard, using convolutional neural network (CNN) and data augmentation techniques. Three classes were used to describe the drill state: red -- for drill that is worn out and should be replaced, yellow -- for state in which the system should send a warning to the operator, indicating that this element should be checked manually, and green -- denoting the drill that is still in good condition, which allows for further use in the production process. The presented method combines the advantages of transfer learning and data augmentation methods to improve the accuracy of the received evaluations. In contrast to the classical deep learning methods, transfer learning requires much smaller training data sets to achieve acceptable results. At the same time, data augmentation customized for drill wear recognition makes it possible to expand the original dataset and to improve the overall accuracy. The experiments performed have confirmed the suitability of the presented approach to accurate class recognition in the given problem, even while using a small original dataset.

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Image Segmentation Using Encoder-Decoder with Deformable Convolutions

Sensors ◽

10.3390/s21051570 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1570

Author(s):

Andreea Gurita ◽

Irina Georgiana Mocanu

Keyword(s):

Neural Network ◽

Image Analysis ◽

Image Segmentation ◽

Convolutional Neural Network ◽

Data Augmentation ◽

Real Life ◽

Semantic Segmentation ◽

Essential Step ◽

Augmentation Techniques ◽

Made In

Image segmentation is an essential step in image analysis that brings meaning to the pixels in the image. Nevertheless, it is also a difficult task due to the lack of a general suited approach to this problem and the use of real-life pictures that can suffer from noise or object obstruction. This paper proposes an architecture for semantic segmentation using a convolutional neural network based on the Xception model, which was previously used for classification. Different experiments were made in order to find the best performances of the model (eg. different resolution and depth of the network and data augmentation techniques were applied). Additionally, the network was improved by adding a deformable convolution module. The proposed architecture obtained a 76.8 mean IoU on the Pascal VOC 2012 dataset and 58.1 on the Cityscapes dataset. It outperforms SegNet and U-Net networks, both networks having considerably more parameters and also a higher inference time.

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Random Erasing Data Augmentation

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.7000 ◽

2020 ◽

Vol 34 (07) ◽

pp. 13001-13008 ◽

Cited By ~ 32

Author(s):

Zhun Zhong ◽

Liang Zheng ◽

Guoliang Kang ◽

Shaozi Li ◽

Yi Yang

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Image Classification ◽

Data Augmentation ◽

Parameter Learning ◽

Identification Code ◽

Training Images ◽

Augmentation Techniques

In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN). In training, Random Erasing randomly selects a rectangle region in an image and erases its pixels with random values. In this process, training images with various levels of occlusion are generated, which reduces the risk of over-fitting and makes the model robust to occlusion. Random Erasing is parameter learning free, easy to implement, and can be integrated with most of the CNN-based recognition models. Albeit simple, Random Erasing is complementary to commonly used data augmentation techniques such as random cropping and flipping, and yields consistent improvement over strong baselines in image classification, object detection and person re-identification. Code is available at: https://github.com/zhunzhong07/Random-Erasing.

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Fault Detection of Supermarket Refrigeration Systems Using Convolutional Neural Network

IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society ◽

10.1109/iecon43393.2020.9254485 ◽

2020 ◽

Author(s):

Zahra Soltani ◽

Kresten Kjaer Soerensen ◽

John Leth ◽

Jan Dimon Bendtsen

Keyword(s):

Neural Network ◽

Fault Detection ◽

Convolutional Neural Network

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ArcNet: Series AC Arc Fault Detection Based on Raw Current and Convolutional Neural Network

IEEE Transactions on Industrial Informatics ◽

10.1109/tii.2021.3069849 ◽

2021 ◽

pp. 1-1

Author(s):

Yao Wang ◽

Linming Hou ◽

Kamal Chandra Paul ◽

Yunsheng Ban ◽

Chen Chen ◽

...

Keyword(s):

Neural Network ◽

Fault Detection ◽

Convolutional Neural Network ◽

Ac Arc

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Prediction of pneumonia COVID19 using a custom convolutional neural network with data augmentation

10.1063/5.0045329 ◽

2021 ◽

Author(s):

Budi Dwi Satoto ◽

Mohammad Imam Utoyo ◽

Riries Rulaningtyas

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Data Augmentation

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Morphological residual convolutional neural network (M-RCNN) for intelligent recognition of wear particles from artificial joints

Friction ◽

10.1007/s40544-021-0516-2 ◽

2021 ◽

Author(s):

Xiaobin Hu ◽

Jian Song ◽

Zhenhua Liao ◽

Yuhong Liu ◽

Jian Gao ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Data Augmentation ◽

Support Vector ◽

Wear Particles ◽

Artificial Joints ◽

Performance Deterioration ◽

Overall Performance ◽

Correct Category ◽

Intelligent Recognition

AbstractFinding the correct category of wear particles is important to understand the tribological behavior. However, manual identification is tedious and time-consuming. We here propose an automatic morphological residual convolutional neural network (M-RCNN), exploiting the residual knowledge and morphological priors between various particle types. We also employ data augmentation to prevent performance deterioration caused by the extremely imbalanced problem of class distribution. Experimental results indicate that our morphological priors are distinguishable and beneficial to largely boosting overall performance. M-RCNN demonstrates a much higher accuracy (0.940) than the deep residual network (0.845) and support vector machine (0.821). This work provides an effective solution for automatically identifying wear particles and can be a powerful tool to further analyze the failure mechanisms of artificial joints.

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