Multi-chips parallel IGBT module is widely used in new energy power grid connection, flexible AC and DC transmission, electric vehicles and charging devices, rail transit electric traction and other fields, and the re...
Multi-chips parallel IGBT module is widely used in new energy power grid connection, flexible AC and DC transmission, electric vehicles and charging devices, rail transit electric traction and other fields, and the reliability of their operation is very important. Therefore, how to select the characteristic parameters that can characterize the aging state of multi-chips parallel IGBT device is particularly important for system active operation and maintenance and reliability improvement. This paper takes the widely used multi-chips parallel IGBT module as the research object. According to the failure mode of bond wire lift-off, the degradation law of electrothermal and magnetic characteristic parameters in the aging failure process of the multi-chips parallel IGBT module was studied, and finally the magnetic flux density was selected as the aging state monitoring parameter of the multi-chips parallel IGBT module.
In this work, a reflection phase gradient metasurface is proposed to realize wideband backscattering enhancement performance. Based on the generalized Snell's law, the phase difference between adjacent metasurface...
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To improve energy storage performance of the multi-ring RCP flywheel comprised of inner hub, tungsten alloy ring and outer retainer, optimization design process for the radial thicknesses of components and interferenc...
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The existing fault diagnosis methods based on deep neural networks mostly rely on process data for fault classification and diagnosis, which makes it difficult to distinguish minor faults with similar data features. T...
The existing fault diagnosis methods based on deep neural networks mostly rely on process data for fault classification and diagnosis, which makes it difficult to distinguish minor faults with similar data features. Therefore, this paper proposes a deep learning-based fault diagnosis model that combines statistical indicators and neural networks. By analyzing and calculating the relevant information entropy to screen the statistical signal features of time domain and frequency domain, we can extract more hidden information from the raw data, and use the convolutional autoencoder to compress the statistical indicators for feature extraction, and use CNN network to extract features from raw data. Finally, the two sets of features are fused and input into the LSTM network to obtain temporal information for fault diagnosis. Tests conducted on the Tennessee Eastman (TE) process show that the proposed model outperforms typical classifiers and improves the diagnostic accuracy of minor faults.
The cubic silicon carbide (3C-SiC) has been considered as a candidate structural material for several types of advanced nuclear reactors. The effects of cascade collision on thermal conductivity in symmetrical tilt gr...
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Bulky sample was fabricated by electron beam rapid manufacture (EBRM) technology, in which Ф1.6 mm wire of in-situ TiB2/Al-Si compositeswas selected as deposition metal, following byT6 heat treatment. The microstruct...
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The NACIE-UP facility is located at the ENEA Brasimone Research Centre and is designed to investigate the thermo-hydraulic properties of LBE within a wire-spaced assembly under uniform and non-uniform power distributi...
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The gas-liquid two-phase flow behaviors are always associated with its dynamic void fraction, such as flow resistance, heat transfer coefficient, phase distribution, critical heat flux etc. As regard to the commercial...
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A theoretical model for Density Wave Oscillations (DWOs) flow instability in parallel rectangular channels under periodic heaving motion is established with a lumped mathematical model based on homogenous hypothesis. ...
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