Fault Diagnosis of Induction Motors with Imbalanced Data Using Deep Convolutional Generative Adversarial Network

Fault Diagnosis of Induction Motors with Imbalanced Data Using Deep Convolutional Generative Adversarial Network

A homemade defective model of an induction motor was created by the laboratory team to acquire the vibration acceleration signals of five operating states of an induction motor under different loads. Two major learning models, namely a deep convolutional generative adversarial network (DCGAN) and a...

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Bibliographic Details
Main Authors: Hong-Chan Chang, Yi-Che Wang, Yu-Yang Shih, Cheng-Chien Kuo
Format: Article
Language:English
Published: MDPI AG 2022-04-01
Series:Applied Sciences
Subjects:
fault diagnosis
neural networks
unsupervised learn fault diagnosis
frequency-domain analysis
Online Access:https://www.mdpi.com/2076-3417/12/8/4080

Internet

https://www.mdpi.com/2076-3417/12/8/4080

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