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.

Abstract
1. 서 론
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. 결 론
후 기
References

• 박창규(School of Mechanical & Automotive Engineering, Gwangju University) | Chang-Kyu Park Corresponding author