Application of deep neural network and generative adversarial network to industrial maintenance: A case study of induction motor fault detection
| Autor: | Yong Oh Lee, Jun Jo, Jongwoon Hwang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Computer science business.industry 020208 electrical & electronic engineering Visibility (geometry) 02 engineering and technology Machine learning computer.software_genre Fault (power engineering) Maintenance engineering Fault detection and isolation 020901 industrial engineering & automation Software deployment 0202 electrical engineering electronic engineering information engineering Oversampling Artificial intelligence business computer Induction motor |
| Zdroj: | IEEE BigData |
| DOI: | 10.1109/bigdata.2017.8258307 |
| Popis: | As data visibility in factories has increased with the deployment of sensors, data-driven maintenance has become popular in industries. Machine learning has been a promising tool for fault detection, but the problem is that the amount of fault data is much less than that of normal data which causes a data imbalance. In this study, we designed a deep neural network for fault detection and diagnosis, and compared the oversampling by a generative adversarial network to standard oversampling techniques. Simulation results indicate that oversampling by the generative adversarial network performs well under the given condition and the deep neural network designed is capable of classifying the faults of an induction motor with high accuracy. |
| Databáze: | OpenAIRE |
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