IGBT remaining useful life prediction method

Abstract

The present invention discloses an IGBT remaining useful life prediction method. By a phase space framing reconstruction technology, on the basis of reconstructing a phase space according to a differential entropy rate, the reconstructed phase space is subjected to Volterra-series input signal vector arrangement; correlation between input data and target output is considered, the optimal selection of each frame of input data is carried out, and the IGBT remaining useful life prediction method adopts a forward-backward algorithm and a least angle regression algorithm which are mature currently to use the optimal input data in input vectors as inputs of a model; a multiresponse sparse regression algorithm and a one-by-one extraction method are added on the basis of an original ELM model to cut off hidden layer nodes which are useless or have little effects, and three mixed neuronal activation functions are used, so that an established network has higher robustness and generalization. According to the present invention, difference of different inputs on a prediction model is sufficiently considered; the prediction model capable of dynamically updating each set of input data by an adaptive algorithm is designed; and prediction accuracy is greatly improved.

Description

technical field [0001] The invention belongs to the technical field of reliability analysis of new power semiconductor devices, and more specifically, a method for predicting the remaining life of an IGBT. Background technique [0002] Insulated Gate Bipolar Transistor (IGBT, Insulated Gate Bipolar Transistor), as a power switching device, has many advantages such as high current density and saturation voltage drop, and has been widely used in many key fields such as new energy power generation and high-voltage transmission. [0003] As the core part of the system, the reliability of the IGBT affects the operation stability of the entire system equipment, which makes the research on the prediction method of the remaining useful life (RUL, Remaining Useful Life) of the IGBT very necessary, which has the following important significance : (1) It is an important way to obtain IGBT reliability information, which can further provide a basis for the realization of system online mo...

AI Technical Summary

Problems solved by technology

However, there are still relatively few data-driven IGBT remaining life prediction methods, and the prediction accuracy needs to be improved.

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