# Genetic Algorithm-Optimized CNN-BiLSTM Framework for Predicting the Remaining Useful Life of IGBT Modules

## TL;DR

This paper introduces a new method using a genetic algorithm to optimize a CNN-BiLSTM model for predicting the remaining useful life of IGBT modules.

## Contribution

The novel contribution is a genetic algorithm-optimized CNN-BiLSTM framework for improved RUL prediction of IGBTs.

## Key findings

- The proposed CNN-BiLSTM model outperforms benchmark algorithms in RUL prediction metrics.
- Genetic algorithm optimization enhances training efficiency and parameter tuning speed.

## Abstract

To address the aging and failure issues that arise during the long-term operation of insulated gate bipolar transistors (IGBTs), this paper proposes a method for predicting their remaining useful life (RUL). The proposed method utilizes a genetic algorithm to optimize a hybrid model that combines a convolutional neural network (CNN) with a bidirectional long short-term memory (BiLSTM) network. First, based on the failure mechanism of IGBTs, various commonly used RUL prediction methods are analyzed and compared. Considering that CNNs are particularly effective at extracting spatial features, while LSTMs excel at capturing long-term dependencies in time-series data, a hybrid CNN-BiLSTM model is developed for RUL prediction, with hyperparameters, including the initial learning rate, optimized using a genetic algorithm. Experimental results demonstrate that the proposed CNN-BiLSTM model achieves superior performance across all metrics compared with benchmark algorithms, and the genetic algorithm significantly accelerates the parameter optimization process and enhances the overall training efficiency.