Hefei University of Technology

Yigang He

Chaolong Zhang

Zhigang Li

Zhijie Yuan

Deqin Zhao

Lei Zuo

Wei He

Bing Li

Zhen Cheng

Liulu He

Baiqiang Yin

Lifen Yuan

Lei Wu

Peng Chen

Yesheng Sun

Shuai Luo

Ying Long

Tao Wang

Qiwu Luo

Tiancheng Shi

Sheng Xiang

Kaiwei Li

Luqiang Shi

A method for monitoring an online state of a bonding wire of an Insulated Gate Bipolar Translator (IGBT) module comprises the following steps: Step 1, constructing a full bridge inverter circuit and an online measuring circuit and connecting two input ends of the online measuring circuit to a collecting electrode and an emitting electrode of an IGBT power module of the full bridge inverter circuit to realize a connection of the full bridge inverter circuit and the online measuring circuit; Step 2, establishing a three-dimensional data model of a healthy IGBT; Step 3, establishing a three-dimensional data model of the IGBT with a broken bonding wire; Step 4, optimizing a least squares support vector machine by adopting a genetic algorithm; and Step 5, estimating states of the three-dimensional data models obtained in the Step 2 and the Step 3 by utilizing the optimized least squares support vector machine.

Method for monitoring online state of bonding wire of IGBT module

A method for diagnosing analog circuit fault based on vector-valued regularized kernel function approximation, includes steps of: step (1) acquiring a fault response voltage signal of an analog circuit; step (2) carrying out wavelet packet transform on the collected signal, and calculating a wavelet packet coefficient energy value as a characteristic parameter; step (3) utilizing a quantum particle swarm optimization algorithm to optimize a regularization parameter and kernel parameter of vector-valued regularized kernel function approximation, and training a fault diagnosis model; and step (4) utilizing the trained diagnosis model to recognize circuit faults.

Method for diagnosing analog circuit fault based on vector-valued regularized kernel function approximation

A method for diagnosing analog circuit fault based on cross wavelet features includes steps of: inputting an excitation signal to an analog circuit under test, and collecting time domain response output signals to form an original data sample set; dividing the original data sample set into a training sample set and a test sample set; performing cross wavelet decomposition on both sets; applying bidirectional two-dimensional linear discriminant analysis to process the wavelet cross spectra of the training sample set and the test sample set, and extracting fault feature vectors of the training sample set and the test sample set; submitting the fault feature vectors of the training sample set to a support vector machine for training an SVM classifier, constructing a support vector machine fault diagnosis model; and inputting the fault feature vectors of the test sample set into the model to perform fault classification.

Method for diagnosing analog circuit fault based on cross wavelet features

A vector-valued regularized kernel function approximation (VVRKFA) based fault diagnosis method for an analog circuit comprises the following steps: (1) obtaining fault response voltage signals of an analog circuit; (2) performing wavelet packet transform on the collected signals, and calculating wavelet packet coefficient energy values as feature parameters; (3) optimizing regularization parameters and kernel parameters of VVRKFA by using a quantum particle swarm optimization algorithm and training a fault diagnosis model; and (4) identifying a circuit fault by using the trained diagnosis model. In the invention, the classification performance of the VVRKFA method is superior to other classification algorithms, and optimization of parameters by the quantum particle swarm optimization (QPSO) algorithm is also superior to the traditional method of obtaining parameters. The fault diagnosis method provided by the invention can efficiently diagnose the component faults of the circuit, including soft faults and hard faults.

Vector-valued regularized kernel function approximation based fault diagnosis method for analog circuit

An analog-circuit fault diagnosis method based on continuous wavelet analysis and an ELM network comprises: data acquisition: performing data sampling on output responses of an analog circuit respectively through Multisim simulation to obtain an output response data set; feature extraction: performing continuous wavelet analysis by taking the output response data set of the circuit as training and testing data sets respectively to obtain a wavelet time-frequency coefficient matrix, dividing the coefficient matrix into eight sub-matrixes of the same size, and performing singular value decomposition on the sub-matrixes to calculate a Tsallis entropy for each sub-matrix to form feature vectors of corresponding faults; and fault classification: submitting the feature vector of each sample to the ELM network to implement accurate and quick fault classification. The method of the invention has a better effect on extracting the circuit fault features and can be used to implement circuit fault classification accurately and efficiently.

Analog-circuit fault diagnosis method based on continuous wavelet analysis and ELM neural network

An analog circuit fault mode classification method comprises the following implementation steps: (1) collecting M groups of voltage signal sample vectors Vto each of fault modes Fof the analog circuit by using a data collection board; (2) sequentially extracting fault characteristic vectors Vof the voltage signal sample vectors Vby using subspace projection; (3) standardizing the extracted fault characteristic vectors Vto obtain standardized fault characteristic vectors; (4) constructing a fault mode classifier based on a support vector machine, inputting the standardized fault characteristic vectors, performing learning and training on the classifier, and determining structure parameters of the classifier; and (5) completing determination of fault modes according to fault mode determination rules. The fault mode classifier of the present invention is simple in learning and training and reliable in mode classification accuracy.

Analog circuit fault mode classification method

An analog circuit fault diagnosis method using a single testable node comprises the following steps: (1) obtaining prior sample data vectors under each fault mode; (2) computing a statistical average of the prior sample data vectors under each of the fault modes; (3) decomposing a signal by an orthogonal Haar wavelet filter set; (4) extracting the feature factor of the prior sample fault modes; (5) extracting a fault-mode-to-be-tested feature factor; (6) computing a correlation coefficient matrix and correlation metric parameters between the feature factor of the prior sample fault modes and the feature factor of the fault-mode-to-be-tested; and (7) determining a fault mode according to a maximal correlation principle by comparing the correlation metric parameters. The method can convert a single signal into a plurality of signals without losing original measurement information, and extract an independent fault mode feature factor reflecting variations of a circuit structure in different fault modes, can be used to study an associated mode determination rule and successfully complete classification of circuit fault modes.

Analog circuit fault diagnosis method using single testable node

A method for predicting a remaining useful life of a lithium battery based on a wavelet denoising and a relevance vector machine, relating to a method for estimating health condition and predicting remaining useful life of lithium battery, includes steps of: (1) obtaining health condition data of each of charge-discharge cycles of the lithium battery by measurement; (2) processing capacity data measured of the lithium battery with wavelet double denoising; (3) calculating a capacity threshold where the lithium battery fails; (4) referring to capacity data and charge-discharge cycle data of the lithium battery, applying a differential evolution algorithm for optimizing a width factor of the relevance vector machine; and (5) predicting the remaining useful life of the lithium battery with the relevance vector machine optimized by the differential evolution algorithm. The method is simple and effective, which can accurately predict remaining useful life of lithium battery.

Method for predicting remaining useful life of lithium battery based on wavelet denoising and relevance vector machine

A wireless sensor network sensor network charging method for multi-charge nodes, including the following steps: (1) establishing a WSNs model; (2) dividing field ranges of charging trolleys; and (3) charging the charging trolleys: (a) initializing: l=0 and j=0, wherein l is the total number of received alarm nodes, and j is the serial number of an alarmed node; (b) receiving an alarm signal, updating values of l and j, generating a shortest charging path s, and computing an energy discriminate vector; (c) if a vector element satisfies Q&#x2264;5% B and j=1, 2, . . . , l, then executing a charging task on l alarm nodes according to the shortest charging path scorresponded thereby, otherwise, returning to continuously update the values of l and j; (d) executing the charging task; and (e) determining whether the charging trolleys are required to go back to a parking lot for recharging, enabling the charging trolleys to go back to the parking lot for recharging if yes, and otherwise, returning to continuously update the values of l and j and the vector element Q.

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Anhui, China

Yigang He