An IGBT lifetime prediction method based on a GA-Elman-LSTM combined model
By optimizing IGBT aging data prediction using a combined GA-Elman-LSTM model, the problems of complex and time-consuming models and low prediction accuracy in existing technologies are solved, achieving high-precision IGBT lifetime prediction.
CN115964937BActive Publication Date: 2026-05-26SOUTHWEST JIAOTONG UNIV
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Patent Information
- Application Number
- CN202211588473.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-12-12
- Publication Date
- 2026-05-26
- Estimated Expiration
- 2042-12-12
AI Technical Summary
Technical Problem
Existing IGBT lifetime prediction technologies suffer from complex and time-consuming models, poor versatility, and difficulty in accurately extracting IGBT aging data information from individual prediction models, resulting in low prediction accuracy and poor versatility.
Method used
A combined GA-Elman-LSTM model is adopted, which optimizes the Elman neural network training process through a genetic algorithm and combines it with an LSTM model for error correction, thereby establishing a combined prediction model to improve prediction accuracy and stability.
Benefits of technology
It effectively improves the accuracy and precision of IGBT lifetime prediction, reduces prediction risks, and enables efficient online prediction and model deployment.
✦ Generated by Eureka AI based on patent content.
Abstract
This invention discloses an IGBT lifetime prediction method based on a GA-Elman-LSTM combined model. Specifically, the method involves: acquiring IGBT aging data; preprocessing the data; using a GA-optimized Elman neural network to import the training dataset into the GA-Elman model for training; constructing error correction training and validation sets using the trained GA-Elman preliminary prediction model; training the LSTM error correction prediction model; and building a GA-Elman-LSTM error correction combined model based on the GA-Elman preliminary prediction model and the LSTM error correction prediction model for final combined prediction. This invention conveniently and efficiently enables the model to converge to the global optimum, effectively improving the model's prediction accuracy; it also improves the model's prediction stability and reduces prediction risk.
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