Next Article in Journal
Interoperability between Building Information Modelling (BIM) and Building Energy Model (BEM)
Next Article in Special Issue
Learning Local Descriptor for Comparing Renders with Real Images
Previous Article in Journal
Production Data Analysis of Hydraulically Fractured Horizontal Wells from Different Shale Formations
Previous Article in Special Issue
Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks
Article

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

Department of Electronics Engineering, Kookmin University, Seoul 02707, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Lidia Jackowska-Strumillo
Appl. Sci. 2021, 11(5), 2166; https://doi.org/10.3390/app11052166
Received: 2 February 2021 / Revised: 22 February 2021 / Accepted: 25 February 2021 / Published: 1 March 2021
(This article belongs to the Special Issue Machine Learning in Computer Engineering Applications)
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. View Full-Text
Keywords: generative adversarial network; data augmentation; machine fault detection generative adversarial network; data augmentation; machine fault detection
Show Figures

Figure 1

MDPI and ACS Style

Bui, V.; Pham, T.L.; Nguyen, H.; Jang, Y.M. Data Augmentation Using Generative Adversarial Network for Automatic Machine Fault Detection Based on Vibration Signals. Appl. Sci. 2021, 11, 2166. https://doi.org/10.3390/app11052166

AMA Style

Bui V, Pham TL, Nguyen H, Jang YM. Data Augmentation Using Generative Adversarial Network for Automatic Machine Fault Detection Based on Vibration Signals. Applied Sciences. 2021; 11(5):2166. https://doi.org/10.3390/app11052166

Chicago/Turabian Style

Bui, Van, Tung L. Pham, Huy Nguyen, and Yeong M. Jang. 2021. "Data Augmentation Using Generative Adversarial Network for Automatic Machine Fault Detection Based on Vibration Signals" Applied Sciences 11, no. 5: 2166. https://doi.org/10.3390/app11052166

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop