Construction method of converter device IGBT residual service life prediction model

A technology of life prediction model and variable flow device, which is applied in the direction of neural learning method, biological neural network model, neural architecture, etc., can solve the problems of poor generalization and doubts in the generalization ability of the model, and achieve strong generalization , Avoid major operational accidents, improve the effect of accuracy

Active Publication Date: 2020-08-28
CRRC YONGJI ELECTRIC CO LTD
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Problems solved by technology

There are many human factors in the establishment of the existing prediction method model, and the generalization ability of the corresponding model

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  • Construction method of converter device IGBT residual service life prediction model
  • Construction method of converter device IGBT residual service life prediction model
  • Construction method of converter device IGBT residual service life prediction model

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Experimental program
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Embodiment Construction

[0030] The construction method of the remaining service life prediction model of the IGBT of the converter device is realized by the following steps:

[0031] Step 1. Collect IGBT accelerated aging data

[0032] On the high and low temperature standard test bench, change the IGBT within the test temperature range, from the minimum temperature to the maximum temperature as a cycle (according to the test standard: minimum temperature T min= -40℃; maximum temperature T max =125℃), the period of each cycle is the same (the period of each cycle is 4 hours); a set of characteristic parameter data sets are collected after each cycle: emitter-collector saturation voltage drop V ce(sat) , gate-emitter threshold voltage V GE（th） , collector current I CE（on） , diode conduction voltage drop V f , gate saturation current IG（sat） and thermal impedance Z th ; Multiple cycles until all the characteristic parameters in the characteristic parameter data set reach the degradation judgment t...

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Abstract

The invention relates to a construction method of a converter device IGBT residual service life prediction model, in particular to a construction method of a rail transit converter device IGBT residual service life prediction model based on a long-short-term memory network. The problem that the LSTM is used for constructing the remaining service life prediction model of the IGBT of the converter device is solved. The construction method is realized by the following steps: 1, collecting IGBT accelerated aging data; 2, performing data normalization processing; 3, constructing and training a long-term and short-term memory network; and 4, verifying the prediction model. By determining the characteristic parameters, constructing the LSTM deep network architecture and specifying the network training parameters, the construction method obtains an IGBT residual service life prediction model of the converter device which reaches the required prediction error index. The prediction model constructed by the construction method is based on a long-short-term memory network and is applied to prediction of the residual service life of an IGBT of a converter device, especially a traction converterof rail transit.

Description

technical field [0001] The invention relates to a method for constructing a forecasting model for the remaining service life of an IGBT of a converter device, in particular to a method for constructing a forecasting model for the remaining service life of an IGBT for a rail transit converter device based on a long-short-term memory network. Background technique [0002] Rail transit converters consist of many electrical components, but the core of rectifiers and inverters is power electronic devices, mainly insulated gate bipolar transistors (IGBTs). Since the IGBT came out in 1985, it has become the mainstream switching device for semiconductor converters. The reliability of the traction converter directly affects the reliable operation of rolling stock, and the failure of IGBT is an important factor affecting the reliability of the main converter. According to foreign statistics, in the faults of industrial converters, the IGBT fault rate accounts for more than 30%, and t...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06N3/04G06N3/08G06F119/02
CPCG06F30/27G06N3/08G06F2119/02G06N3/045
Inventor 王昭李骁猛刘谆侯涛吴晓威
Owner CRRC YONGJI ELECTRIC CO LTD
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