인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
This study investigates vibration signal-based fault diagnosis for high-speed spindle motors operating at 10,000 60,000 RPM, which exhibit vibration patterns distinct from conventional lower-speed motors. The analysis focuses on vibration characteristics, signal preprocessing, and deep learning model performance. Vibration signals are transformed into the frequency domain using the fast fourier transform (FFT), while time-domain features such as root mean square (RMS) and peak-to-peak amplitude are extracted. To address data distributional variations, normalization techniques, including MinMaxScaler, StandardScaler, and RobustScaler, are applied before model training. Subsequently, multiple architectures are evaluated, including convolutional neural networks (CNN), long short-term memory networks (LSTM), and hybrid variants such as CNN-LSTM, LSTM-CNN, bidirectional LSTM (BiLSTM), CNN-BiLSTM, and BiLSTM-CNN. Experimental results highlight that CNN-LSTM and CNN-BiLSTM yield the highest classification accuracy with efficient inference times. These findings demonstrate the efficacy of combining advanced signal preprocessing with deep learning for fault diagnosis, offering valuable contributions to the development of robust predictive maintenance strategies, enhancing the safety, reliability, and efficiency of industrial systems employing high-speed spindle motors.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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UCI(KEPA) : I410-151-26-02-094401380
