A Representation Generation Approach of Transmission Gear Based on Conditional Generative Adversarial Network

Li, Jie; Zhao, Boyu; Wu, Kai; Dong, Zhicheng; Zhang, Xuerui; Zheng, Zhi-Hao

doi:10.3390/act10050086

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…Its network structure is shown in Fig. 1: The main structure of GAN includes a generator and a discriminator [37][38]. The generator is used to generate real samples, and the discriminator is used to determine real samples and fake samples.…”

Section: Theoretical Methods Introduction 21 Deep Convolutional Generative Adversarial Network(dcgan)mentioning

confidence: 99%

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Gas Turbine Rotor System Fault Diagnosis Method based on Improved Deep Convolutional Generative Adversarial Networks

Liu

Wang

Han

et al. 2021

Preprint

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In gas turbine rotor system fault diagnosis intelligent method based on data-driven is an important means to monitor the health status of gas turbine, it is necessary to obtain sufficient effective fault data to train the intelligent diagnosis model. In the actual operation of gas turbine, the collected gas turbine fault data is limited, and the small and imbalanced fault samples seriously affect the accuracy of fault diagnosis method. Aiming at the imbalance of gas turbine fault data, an Improved Deep Convolutional Generative Adversarial Network (Improved DCGAN) suitable for gas turbine signal is proposed, an structural optimization on generator of Deep Convolutional Generative Adversarial Network (DCGAN) and gradient penalty improvement on the loss function are introduced to generate effective fault data and improve the classification accuracy. The experiment results of gas turbine test bench demonstrated that the proposed method generated effective fault samples as a supplementary set of fault samples to balance the dataset, effectively improved the fault classification and diagnosis performance of gas turbine rotor in the case of small samples, The proposed method can be used as a solution to the problems of small unbalanced fault samples, and provides an effective method for gas turbine fault diagnosis.

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Section: Theoretical Methods Introduction 21 Deep Convolutional Generative Adversarial Network(dcgan)mentioning

confidence: 99%

“…Fig.1 GAN network structure diagramThe main structure of GAN includes a generator and a discriminator[37][38]. The generator is used to generate…”

mentioning

confidence: 99%

Gas Turbine Rotor System Fault Diagnosis Method based on Improved Deep Convolutional Generative Adversarial Networks

Liu

Wang

Han

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

Deep Convolutional Generative Adversarial Network-Based Plant Disease Detection And Classification Using Particle Swarm Optimization Algorithms

Pratap,

Kumar

2023

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT)

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Research on Improved Deep Convolutional Generative Adversarial Networks for Insufficient Samples of Gas Turbine Rotor System Fault Diagnosis

2022

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In gas turbine rotor systems, an intelligent data-driven fault diagnosis method is an important means to monitor the health status of the gas turbine, and it is necessary to obtain sufficient fault data to train the intelligent diagnosis model. In the actual operation of a gas turbine, the collected gas turbine fault data are limited, and the small and imbalanced fault samples seriously affect the accuracy of the fault diagnosis method. Focusing on the imbalance of gas turbine fault data, an Improved Deep Convolutional Generative Adversarial Network (Improved DCGAN) suitable for gas turbine signals is proposed here, and a structural optimization of the generator and a gradient penalty improvement in the loss function are introduced to generate effective fault data and improve the classification accuracy. The experimental results of the gas turbine test bench demonstrate that the proposed method can generate effective fault samples as a supplementary set of fault samples to balance the dataset, effectively improve the fault classification and diagnosis performance of gas turbine rotors in the case of small samples, and provide an effective method for gas turbine fault diagnosis.

show abstract

A Representation Generation Approach of Transmission Gear Based on Conditional Generative Adversarial Network

Cited by 3 publications

References 32 publications

Gas Turbine Rotor System Fault Diagnosis Method based on Improved Deep Convolutional Generative Adversarial Networks

Gas Turbine Rotor System Fault Diagnosis Method based on Improved Deep Convolutional Generative Adversarial Networks

Deep Convolutional Generative Adversarial Network-Based Plant Disease Detection And Classification Using Particle Swarm Optimization Algorithms

Research on Improved Deep Convolutional Generative Adversarial Networks for Insufficient Samples of Gas Turbine Rotor System Fault Diagnosis

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