Earticle

초록

영어

This paper presents an AI-based PHM (Prognostics and Health Management) framework for quantitative motor health assessment and remaining useful life (RUL) prediction. The proposed method first defines a health index using vibration and current signals of an industrial motor, and then adopts a two-stage PHM architecture consisting of health-state classification and deep learning-based RUL prediction. A degradation test bench is designed to obtain condition monitoring data for normal, warning, and critical states, and a hybrid 1D CNN–BiLSTM–attention model is developed to capture both local features and long-term temporal dependencies. Experimental results demonstrate that the proposed model outperforms conventional SVM and single LSTM baselines in terms of both health-state classification accuracy and RUL prediction accuracy, achieving a 20–30% reduction in RMSE and more than 80% of RUL predictions within ±10% error. The proposed approach provides a practical PHM framework and modeling guidelines for implementing condition-based maintenance of electric motors in smart manufacturing environments.

목차

Abstract
1. 서론
2. 딥러닝 기반 예지진단 기술 동향
3. 연구방법
3.1 제안한 PHM모델
3.2 데이터 전처리
3.3 CNN-BiLSTM-Attention 모델 구조
4. 실험결과 및 분석
4.1 실험환경
4.2 비교모델 구성
4.3 상태 분류 성능 결과 및 분석
4.4 RUL 예측 성능 결과 및 분석
5. 결론
References

키워드

저자

• 박창규 [ Chang-Kyu Park | Member, School of Mechanical & Automotive Engineering, Gwangju University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국기계항공기술학회지(구 한국기계기술학회지) [Journal of the Korean Society of Mechanical and Aviation Technology ]

• 간기

  격월간

• pISSN

  1229-604X

• eISSN

  2508-3805

• 수록기간

  1999~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 550 DDC 620