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7 results on '"Xie, Minghang"'

2. Online Monitoring Bond Wires Fault of SiC MOSFETs With Kelvin Package Based on Turn-on Source Voltage Ringing

Author

Xie, Minghang, Sun, Pengju, Ouyang, Wenyuan, Luo, Quanming, and Du, Xiong

Abstract

Bond wire fault is one of the most prominent degradation types for silicon carbide (SiC) mosfets, which could be detected and monitored through appropriate precursors. In this article, an online bond wire fault monitoring method for SiC mosfets with Kelvin package is proposed. It is based on the fact that the bond wires parasitic inductances are affected by bond wire fault over time. The variations of parasitic inductances can be converted into the variations of the voltage ringing between the Kelvin source and power source (vKS-S) during the turn-on event. To eliminate the affecting factors, the voltage ringing of the circuit parasitic inductance (vCIR) is introduced, and the division of the peak values of the two voltages ringing is used as the new bond wire fault precursor. The study results indicate that the proposed precursor has high sensitivity and high resolution to monitor each bond wire fault. Besides, the monitoring results are not affected by factors such as junction temperature, load current, and bus voltage. Therefore, the proposed fault monitoring method can be implemented online without knowing the factor information. The confirmatory experiment is carried out to verify the correctness of the proposed method.

Published
2024
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3. Power inspection image enhancement based on fractional wavelet combined with improved QPSO.

Author

Liu, Ziyun, Xie, Minghang, and Yang, Xiaozhong

Subjects
*IMAGE intensifiers, *DISCRETE wavelet transforms, *PARTICLE swarm optimization, *COMPLEX numbers
Abstract

Under uneven lighting conditions, power inspection (PI) images captured by drones exhibit low contrast, lost details and severe noise, which affects the efficiency of monitoring. In response to these issues, this paper proposes a new method for enhancing PI images based on discrete fractional wavelet transform (DFRWT). Firstly, according to the image entropy feature, an appropriate fractional order of DFRWT is selected to decompose the PI image. Secondly, we introduce a low-frequency fusion enhancement method based on complex number domain. It employs different mapping functions to enhance the brightness and contrast of PI image, then the results are combined to reconstruct a new low-frequency component. Here, an improved quantum-behaved particle swarm optimization (QPSO) algorithm is proposed to determine the optimal parameters of the functions, preventing over-enhancement. Finally, an adaptive threshold function is applied to handle noise and edge coefficients in high-frequency components. Using 5 quality metrics to evaluate 8 existing algorithms on 9 datasets, the results indicate that the proposed algorithm can effectively enhance the clarity of PI images, performing excellently in noise suppression and edge detail enhancement. [ABSTRACT FROM AUTHOR]

Published
2023
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