Data Augmentation Using Generative Adversarial Network for Automatic Machine Fault Detection Based on Vibration Signals
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TL;DR: A method of using the generative adversarial network as the fault signal augmentation method to enrich the dataset and conclude that the generated data could be used to mix with original data and improve the model performance.
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Abstract: 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.
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Citations
A Novel Method for Fault Diagnosis of Bearings with Small and Imbalanced Data Based on Generative Adversarial Networks
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Generative Adversarial Networks for Data Augmentation
Angona Biswas,Nasim Md Abdullah Al,Al Imran,Anika Tabassum Sejuty,Fabliha Fairooz,Sai Puppala,Sajedul Talukder +6 more
- 01 Jan 2023
TL;DR: Generative Adversarial Networks (GANs) are used for data augmentation in medical image analysis. They generate synthetic data that resembles real samples, increasing the available dataset for model training.
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GAN-based Data Augmentation for UWB NLOS Identification Using Machine Learning
21 Feb 2022
TL;DR: In this article , an efficient method using Generative Adversarial Network for data augmentation cooperating with autoencoder for enhancing the training model is proposed to reduce the localization error caused by non-line-of-sight condition.
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GAN-based Data Augmentation for UWB NLOS Identification Using Machine Learning
Duc Hoang Tran,Byung-Ki Chung,Yeong Min Jang +2 more
- 21 Feb 2022
TL;DR: An efficient method using Generative Adversarial Network for data augmentation cooperating with autoencoder for enhancing the training model and the results show the framework obtained state-of-art identification performance.
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•Posted Content
The Effectiveness of Data Augmentation in Image Classification using Deep Learning
Luis Perez,Jason Wang +1 more
TL;DR: A method to allow a neural net to learn augmentations that best improve the classifier, which is called neural augmentation is proposed, and the successes and shortcomings of this method are discussed.