A method for predicting the remaining life of igbt

Abstract

The invention discloses a method for predicting the remaining life of an IGBT. By means of the phase space frame reconstruction technology, on the basis of reconstructing the phase space by differential entropy rate, the reconstructed phase space is arranged with input signal vectors of Volterra series; Considering the correlation between the input data and the target output, the optimal selection of the input data of each frame is carried out. The present invention adopts the relatively mature forward-backward algorithm and the minimum angle regression algorithm to select the better input data in the input vector to As the input of the model; on the basis of the original ELM model, add multi-response sparse regression algorithm and one-by-one extraction method to cut out useless or little hidden layer nodes, and use three mixed neuron activation functions, so that The established network is more robust and generalizable; the present invention fully considers the difference between different input pairs of prediction models, and designs an adaptive algorithm to dynamically update the prediction model of each group of input data, which greatly improves the Improved prediction accuracy.

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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