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차량 데이터를 활용한 LSTM-Autoencoder 기반 차량 고장 예측 모델의 성능 개선
손유지(Youji Sohn),김영국(Young-Kuk Kim),이규현(Gyuhyeon Lee) 대한전자공학회 2023 대한전자공학회 학술대회 Vol.2023 No.6
This paper is a study to improve a system for prediction vehicle failures. To overcome the limitations of the existing deep learning-based vehicle data-based fault prediction system and achieve higher performance, we aim to implement a system that utilizes driving history data and fault diagnosis data to predict faults and notify users. The LSTM-Autoencoder model was used to implement the model, and as a result, the present model showed 94.4% accuracy and 98.6% precision. Through these results, this study achieved higher performance compared to the existing fault prediction system, and proposes an advanced system that can be applied to actual industrial sites.