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

Claim 7:
8. The method for monitoring the online state of the bonding wire of the IGBT module according to claim 1, wherein a specific method for estimating the states of the three-dimensional data models by utilizing the optimized least squares support vector machine in the step 5 comprises:
under a same condition, i.e., the forward on current and the environment temperature are same, obtaining a saturation voltage drop increment when A bonding wires of the IGBT are broken according to a difference value between a saturation voltage drop obtained when the A bonding wires of the IGBT are broken and the saturation voltage drop when the IGBT is healthy dividing by the saturation voltage drop when the IGBT is healthy, wherein A is greater than or equal to 1 but smaller than or equal to the total number of the bonding wires;
the failures of the bonding wires of the IGBT modules are divided into three grades according to the saturation voltage drop increment: healthy, bonding wire failure and chip failure, wherein corresponding saturation voltage drop increment intervals respectively are ΔVCE<1%, 1%≤ΔVCE<5% and ΔCE≥5%; labels 1, 2 and 3 respectively represent the three grades: the failures of are bonding wires of the IGBT modules are healthy, the bonding wire failure and the chip failure;
constructing a classifying decision making function of the least squares support vector machine according to an optimum parameter obtained of the least squares support vector machine, wherein an output of the classifying decision making function of the least squares support vector machine is the grade of the failure of the bonding wires of the IGBT modules, the output of the classifying decision making function of the least squares support vector machine is 1, 2 or 3, and 1, 2 or 3 respectively represent three grades: the failed bonding wires of the IGBT modules are healthy, the bonding wire failure and the chip failure;
an input of the classifying decision making function of the least squares support vector machine is the saturation voltage drop of the IGBT power module measured under a working condition, i.e., the environment temperature and the on current are determined;
providing a training sample set {(x1, y1), . . . , (xn,yn)}, wherein n represents a capacity of the training samples, xi∈Rn represents the ith training sample, yi represents an expected output of the ith training sample, i.e., a class label;
one training sample xi corresponds to one three-dimensional array (Ta,Ic,VCE);
when the three-dimensional array (Ta,Ic,VCE) as the training sample xi is obtained according to the saturation voltage drop curved surface of the healthy IGBT power module and the saturation voltage drop increment ΔVCE<1%, the expected output yi of the training sample is equal to 1;
when the three-dimensional array (Ta,Ic,VCE) as the training sample xi is obtained according to the saturation voltage drop curved surfaces of the IGBT with the broken bonding wires (1-3 bonding wires are broken) and the saturation voltage drop increment 1%≤ΔVCE≤5%, the expected output yi of the training sample is equal to 2;
when the three-dimensional array (Ta,Ic,VCE) as the training sample xi is obtained according to the saturation voltage drop curved surfaces of the IGBT with the broken bonding wires (4-6 bonding wires are broken) and the saturation voltage drop increment ΔVCE≥5%, the expected output yi of the training sample is equal to 3;
the classifying decision making function of the least squares support vector machine constructed according to the optimum parameter obtained of the least squares support vector machine is:, f
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wherein Ω represents a weight vector, b represents an offset constant, K(xi,yj) represents a kernel function of the least squares support vector machine, K(xi,xj)=exp(−∥xi−xj∥2/2σ2) is a function of a kernel parameter σ, and xi and xj respectively represent the ith and ith sample inputs, Ω and b can be determined by solving a target function of the least squares support vector machine, the target function being:, min
     
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a constraint condition is as follows:

yi[ωT·φ(xi)+b]=1−ξi,i=1, . . . ,n,, wherein xi and yi respectively represent the ith training sample input and a corresponding output thereof, n represents the capacity of the training samples, γ is a regularization parameter, ξi is a relaxing factor, and ξi≥0, φ(·) is a mapping function of a kernel space.