断层(地质)
计算机科学
鉴别器
永磁同步发电机
自编码
控制理论(社会学)
发电机(电路理论)
人工智能
模式识别(心理学)
电子工程
人工神经网络
功率(物理)
工程类
磁铁
电信
电气工程
物理
控制(管理)
量子力学
地震学
探测器
地质学
作者
Feng Li,Honglin Luo,Shuiqing Xu,Kenan Du
出处
期刊:Electronics
[MDPI AG]
日期:2023-10-08
卷期号:12 (19): 4172-4172
被引量:1
标识
DOI:10.3390/electronics12194172
摘要
In this study, a novel intelligent inverter fault diagnosis approach based on a stacked denoising autoencoder–generative adversarial network–long short-term memory (SDAE-GAN-LSTM) under an imbalanced sample is proposed for a three-phase permanent-magnet synchronous motor (PMSM) drive system. The proposed method can address the problem of unbalanced fault data samples and improve the accuracy of fault classification. Concretely speaking, firstly, the stacked denoising autoencoder (SDAE) is pre-trained to obtain the optimum decoder network. Afterward, a new generator of generative adversarial networks (GANs) is designed to generate high-quality samples by migrating the pre-trained optimal decoder network to the hidden layer and output layer of the generator of GANs. Additionally, a new model of long short-term memory (LSTM) based on the second discriminator of the GANs is presented for fault diagnosis. The generator of GANs is cross-trained using the reconstruction error gained by SDAE and the fault diagnosis error obtained by LSTM, resulting in the generation of high-quality samples for fault discrimination. Simulation and experimental results demonstrate the effectiveness of the proposed fault diagnosis approach, and the average fault identification accuracy reaches 98.63%.
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