Wuhan University

Guolong Shi

Yigang He

Liulu He

Chaolong Zhang

Hui Zhang

Bolun Du

Yongbo Sui

Jiajun Duan

Bing Li

Baiqiang Yin

Xiaoxin Wu

Weibo Yuan

Zhong Li

Hui Shao

Yuan Chen

Luqiang Shi

Shuiqing Xu

Yuting Wu

A transformer failure diagnosis method and system based on an integrated deep belief network are provided. The disclosure relates to the fields of electronic circuit engineering and computer vision. The method includes the following: obtaining a plurality of vibration signals of transformers of various types exhibiting different failure types, retrieving a feature of each of the vibration signals, and establishing training data through the retrieved features; training a plurality of deep belief networks exhibiting different learning rates through the training data and obtaining a failure diagnosis correct rate of each of the deep belief networks; and keeping target deep belief networks corresponding to the failure diagnosis correct rates that satisfy requirements, building an integrated deep belief network through each of the target deep belief networks, and performing a failure diagnosis on the transformers through the integrated deep belief network.

Transformer failure diagnosis method and system based on integrated deep belief network

A method and a system for fault diagnosis with small samples of power equipment based on virtual and real twin spaces are disclosed, which belong to the field of fault diagnosis of power equipment. The method includes: test samples containing different locations, types and severity levels of fault of power equipment are acquired to form a real physical space; a virtual mirror space is acquired by simulation according to a simulation model of the equipment to be diagnosed; the training set in the real physical space is spatially integrated with the sample set in the virtual mirror space to obtain a training sample set in the twin spaces; the training sample set in the twin spaces serves as the supplement to the training set in the real physical space, and the fault type and fault location serve as diagnostic labels to be input to the deep neural network for training.

Method and system for fault diagnosis with small samples of power equipment based on virtual and real twin spaces

A method and a system for online monitoring of a health status of an insulated-gate bipolar transistor (IGBT) module are provided, which belong to the field of IGBT status monitoring. In order to overcome the inability to real-time monitor health statuses of existing IGBT modules, the method of the disclosure includes the following steps. A current sensor is used to measure a collector current of each IGBT module. A collected current value is substituted into a simulation model to obtain a current imbalance rate. A failure module is located according to the current imbalance rate and temperature to achieve the objective of monitoring an IGBT health status.

Method and system for online monitoring of health status of insulated-gate bipolar transistor module

A method and a system for identifying third-order model parameters of a lithium battery based on a likelihood function are provided, which relates to a method for estimating battery model parameters of a lithium battery under different temperatures, different system-on-chips (SOCs), and charge-discharge currents. The method includes the following steps. A third-order battery model of the lithium battery is established. A battery model output voltage Uand a total battery current I under different temperatures, different SOCs, and charge-discharge currents are collected. The likelihood function is adopted to construct an identification model, and the collected data is substituted into the identification model to calculate the battery model parameters. Identified parameters are substituted into the third-order battery model to obtain a battery terminal voltage to be compared with a measured terminal voltage. The operation method of the disclosure is simple and effective, and can accurately estimate internal resistance parameters of the lithium battery.

Method and system for identifying third-order model parameters of lithium battery based on likelihood function

A method for vehicle-loading warehousing asset management based on an ultra-high frequency (UHF) radio frequency identification (RFID) path loss model, which includes the following steps. An electromagnetic wave is emitted by a tag reader. The electromagnetic wave is diffracted, reflected, and scattered when passing through a warehousing vehicle hood, and the electromagnetic wave is emitted and scattered through the ground. A UHF RFID tag attached to a front surface location region of assets receives electromagnetic waves of various paths emitted by the tag reader. The tag reader reads UHF RFID tag information. A transfer function of a tag receiving signal is constructed according to the tag information, and a path loss function during a UHF RFID tag sensing electromagnetic wave process is constructed according to the transfer function. The path loss is calculated according to the constructed path loss function. A location of the UHF RFID tag is obtained.

Method and system for vehicle-loading warehousing asset management based on ultra high frequency radio frequency identification path loss model

Provided are a time domain channel prediction method and a time domain channel prediction system for an OFDM wireless communication system, which relate to the technical field of adaptive transmission in wireless communication. Frequency domain channel information is converted into time domain tap information by inverse Fourier transform. With respect to each time domain tap information, tap information prediction based on an extreme learning machine is realized, and finally predicted tap information is converted into frequency domain channel information by Fourier transform. To improve a generalization ability of a channel predictor, an output weight of the extreme learning machine is punished by a combination of lregularization and lregularization. The disclosure may provide satisfactory prediction performance and may output a sparse output weight, which reduces the requirement for memory storage. The disclosure ensures adaptive transmission and adaptive coding of wireless communication.

Time domain channel prediction method and time domain channel prediction system for OFDM wireless communication system

A channel prediction system and a channel prediction method for an OFDM wireless communication system include a standard echo state network and a two-layer adaptive elastic network. In the method, with respect to each subcarrier of a pilot OFDM symbol, an echo state network is trained by using frequency domain channel information of each subcarrier obtained by channel estimation. The trained echo state network may realize short-term prediction of the frequency domain channel information. To overcome a likely ill-conditioned solution of an output weight in an echo state network, the output weight in the echo state network is estimated by using a two-layer adaptive elastic network.

Channel prediction system and channel prediction method for OFDM wireless communication system

A wireless channel monitoring and simulation device with multi-input multi-output (MIMO) is provided, which includes: a wireless channel monitor, configured to collect characteristic parameters of wireless channels in typical environments, and establish models based on the characteristic parameters; a model database, configured to store the characteristic parameter models and parameterize; an original signal, configured to input N different original signals; a wireless channel simulator, configured to simulate a typical channel environment according to the model database configuration, so that the original signal is the same as that in a real typical channel environment, and adopts a N-path output; an N-channel oscilloscope, configured to observe specific waveforms of N-path simulated signals; and a master computer software, configured to process, analyze, and store N-path output signals. The disclosure has many input and output channels, many simulation channel paths, many observable signal changes, flexible design, and fast signal processing speed.

Wireless channel monitoring and simulation device with multi-input multi-output

A 5G-oriented multidimensional adaptive MIMO system including a transmitting antenna array, a first rotary table, a broadband vector signal generator, a receiving antenna array, a second rotary table, a broadband vector signal analyzer and a data acquisition terminal is provided. The broadband vector signal generator and the broadband vector signal analyzer are respectively connected with the transmitting antenna array and the receiving antenna array. The data acquisition terminal is connected with the broadband vector signal generator and the broadband vector signal analyzer. In addition, a method for adjusting radiating modes of antenna ports by using the 5G-oriented multidimensional adaptive MIMO system is also provided.

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Hubei, China

Guolong Shi