Patent ID: 11874341
Assignee: HEFEI UNIVERSITY OF TECHNOLOGY
Field: Measurement (Instruments)
Classification: CPC G  H | IPC G  H

Claim 6:
7. The method for monitoring the online state of the bonding wire of the IGBT module according to claim 6, wherein a specific method for optimizing the least squares support vector machine by adopting the genetic algorithm in the step 4 comprises:
obtaining a series of the three-dimensional arrays (TaIc,VCE) according to the saturation voltage drop curved surfaces of the healthy IGBT power modules and the saturation voltage drop curved surfaces of the failed IGBT power module with broken bonding wires, dividing all the three-dimensional arrays (Ta,Ic,VCE) obtained, according to the saturation voltage drop curved surfaces of the healthy IGBT power modules and the saturation voltage drop curved surfaces of the failed IGBT power module with broken bonding wires, into two portions: a part of the saturation voltage drop curved surfaces is taken as a training sample and a other part of the saturation voltage drop curved surfaces is taken as a test sample;
wherein the three-dimensional arrays (Ta,Ic,VCE) obtained according to the saturation voltage drop curved surfaces of the failed IGBT power module with broken bonding wires comprise the three-dimensional array (Ta,Ic,VCE) obtained according to the saturation voltage drop curved surface of the failed IGBT power module with one broken bonding wire, the three-dimensional array (Ta,Ic,VCE) obtained according to the saturation voltage drop curved surface of the failed IGBT power module with two broken bonding wires, the three-dimensional array (Ta,Ic,VCE) obtained according to the saturation voltage drop curved surface of the failed IGBT power module with A broken bonding wires, and the three-dimensional array (Ta,Ic,VCE) obtained according to the saturation voltage drop curved surfaces of the failed IGBT power module with all the broken bonding wires, A being greater than or equal to 1 but smaller than or equal to the total number of the bonding wires;
with respect to the least squares support vector machine, different kernel functions show different classifying properties, a Gaussian radial basis (RBF) kernel function in a form of K(xi,xj)=exp(−∥xi−xj∥2/2σ2) is used, wherein σ represents a kernel parameter; selection of the kernel parameter σ of the least squares support vector machine and a regularization parameter γ of the least squares support vector machine affects a classifying precision of the least squares support vector machine, and in order to classifying failures of the bonding wires of the IGBT power modules quickly and accurately, parameters of the least squares support vector machine are optimized by adopting the genetic algorithm:
1) encoding: encoding the training sample with a Gray code;
2) generation of an initial population: randomly generating 50 groups of the kernel parameters σ and the regularization parameters γ of the least squares support vector machine, one group of parameters being a chromosome, 50 chromosomes being in total, σ and γ being two genes on the chromosomes, and initial value ranges of σ and γ being [0.1,100];
3) adaptability value evaluation detection: taking an accuracy obtained, by performing cross validation on the training sample, as a adaptability value of each chromosome in the genetic algorithm;
4) selection: sequencing the adaptability values of the chromosomes from large to small, and selecting the values according to a random competition selection method;
5) crossover: arranging a crossover probability pc=0.5 and selecting two points to crossover;
6) mutation: setting a mutation probability pm=0.01 selecting a new individual, and generated by a valid genetic mutation; and
7) terminating condition: setting a maximum iteration number of times at 200, putting the test sample in the least squares support vector machine, wherein if the accuracy does not reach an index and the iteration number of times is smaller than 200, selection, crossover and mutation operations are performed continuously, and if the accuracy reaches the index or the iteration number of times reaches 200, a chromosome individual with the maximum adaptability value is selected as an optimum parameter of the least squares support vector machine.