Earticle

초록

영어

This study proposes a deep learning–based predictive maintenance model for condition monitoring and remaining useful life (RUL) estimation of a 1 kW brushless DC (BLDC) motor. Multi-sensor signals, including vibration (10 kHz), current (20 kHz), and surface temperature (10 Hz), were acquired under six health conditions: normal, bearing outer race fault (BPFO), bearing inner race fault (BPFI), unbalance, misalignment, and stator insulation degradation. To jointly exploit spatial patterns and temporal degradation behaviors, a hybrid CNN–LSTM model with a multi-task learning framework was developed to perform 6-class fault classification and RUL regression simultaneously. Experimental results on the constructed BLDC motor dataset show that the proposed model achieves a classification accuracy of 95.8%, outperforming conventional SVM and 1D-CNN baselines (85.2% and 90.7%, respectively). In addition, the proposed method significantly reduces RUL prediction error, yielding an RMSE of 9.6 and an MAE of 6.8, which corresponds to approximately 39% improvement over a single LSTM-based regression model. These results demonstrate that the proposed CNN–LSTM multi-sensor fusion framework is effective for intelligent condition monitoring and predictive maintenance of BLDC motor systems, and it can be extended to a wide range of rotating machinery applications.

목차

Abstract
1. 서론
2. 관련 연구 및 이론적 배경
2.1 진동 기반 고전적 상태 진단 기법
2.2 머신러닝 기반 상태 진단 기법
2.3 딥러닝 기반 예지정비(PHM) 기법
2.4 BLDC모터의 예지정비 필요성
3. 실험구성
3.1 시스템 및 센서구성
3.2 고장 시나리오 구성
3.3 데이터 전처리
4. 예지진단 모델 구조
4.1 CNN-LSTM 하이브리드 구조
4.2 다중 태스크 학습(MTL) 구조
5. 실험결과 및 분석
5.1 상태 분류 성능
5.2 RUL 예측 성능
5.3 Ablation Study(제거 연구)
5.4 실험결과 분석 및 고찰
6. 결론
References

키워드

저자

• 박창규 [ Chang-Kyu Park | Member, Dept. 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