Open-Switch Fault Diagnosis of Three-Phase PWM Converter Systems for Magnet Power Supply on EAST

Deng, Xi; Wan, Chenguang; Jiang, Li; Gao, Ge; Huang, Ya

doi:10.1109/tpel.2022.3194113

Cited by 14 publications

(1 citation statement)

References 53 publications

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“…However, this method necessitates the generation of current vector trajectory maps, thereby increasing the complexity of data processing. The work in [ 17 ] applies a CNN based on the inception structure for diagnosing open-circuit faults in PWM converters. However, the diagnostic speed is influenced significantly due to the network’s extensive parallel structure.…”

Section: Introductionmentioning

confidence: 99%

An Open-Circuit Fault Diagnosis Method for Three-Level Neutral Point Clamped Inverters Based on Multi-Scale Shuffled Convolutional Neural Network

Yan,

Wu,

Cao

et al. 2024

Sensors

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This study constructs a power switching device open-circuit fault diagnosis model for a three-level neutral point clamped inverter based on the multi-scale shuffled convolutional neural network (MSSCNN) and extracts and classifies the fault information contained in the output current of inverters. The model employs depthwise separable convolution and channel shuffle techniques to improve diagnostic accuracy and reduce model complexity. The experimental results show that the new model has lower model complexity, better noise resistance and higher average diagnostic accuracy compared with fault diagnosis models based on CNN, ResNet, ShuffleNet V2 and Mobilenet V3 networks.

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Section: Introductionmentioning

confidence: 99%

An Open-Circuit Fault Diagnosis Method for Three-Level Neutral Point Clamped Inverters Based on Multi-Scale Shuffled Convolutional Neural Network

Yan,

Wu,

Cao

et al. 2024

Sensors

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A simple and robust diagnosis method for open-circuit faults of voltage-source inverters based on abnormal voltage sequence

Wu,

Chen,

Tian

et al. 2023

Electr Eng

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A novel Bayesian optimization prediction framework for four-axis industrial robot joint motion state

Zhu,

Liu,

Tan

et al. 2024

Complex Intell. Syst.

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Robot joints are the main structure for controlling the motion of the machine body, where the motion state of them directly affects the performance of the industrial robot. Due to the difficulty of obtaining the joint torque information of industrial robots, it is very hard to monitor the motion state of them. Based on the velocity and force driven by current of motors, we propose a novel Bayesian optimization framework to predict the joint motion state of industrial robot in this paper. Based on the temporal correlation of joint current and the correlation between the current and motion state of joint, we use the LSTM and BiLSTM to regressing prediction of the current and state of joint motor first. Then, the Bayesian optimization method is used to adjust the hyperparameters of our network, which realize the analysis of the joint motor current under different motion states and improve the accuracy of the prediction of joint motion states. Finally, we design the joint current acquisition platform of industrial robot based on Hall current sensors, which can collect joint currents without contact and generate experimental dataset. Comparing with the popular intelligent methods, the results show that our Bayesian optimization framework realizes a more accurate prediction of motion state for the four-axis industrial robot on the basis of contact-less current acquisition.

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Open-Switch Fault Diagnosis of Three-Phase PWM Converter Systems for Magnet Power Supply on EAST

Cited by 14 publications

References 53 publications

An Open-Circuit Fault Diagnosis Method for Three-Level Neutral Point Clamped Inverters Based on Multi-Scale Shuffled Convolutional Neural Network

An Open-Circuit Fault Diagnosis Method for Three-Level Neutral Point Clamped Inverters Based on Multi-Scale Shuffled Convolutional Neural Network

A simple and robust diagnosis method for open-circuit faults of voltage-source inverters based on abnormal voltage sequence

A novel Bayesian optimization prediction framework for four-axis industrial robot joint motion state

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