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
In this study, an Attention-based Long Short-Term Memory (LSTM) Autoencoder model is proposed for semi-supervised anomaly detection in CNC turning processes. The model addresses challenges in highly imbalanced industrial environments where abnormal data account for less than 1% of total samples. Spindle current signals were collected from real machining operations, preprocessed using Root Mean Square transformation, and feature-extracted via the Time Series Feature Extraction Library (TSFEL). The data were reconstructed into sliding windows to preserve temporal dependencies. The proposed hybrid architecture combines an LSTM encoder-decoder, an Attention module, and a parallel classifier trained through a hybrid loss function integrating reconstruction and classification errors. Experimental results showed significant improvements over conventional Autoencoder and LSTM-Autoencoder models, achieving an accuracy and a recall of 0.99. Although the model exhibited near-perfect ROC AUC values, this was intentionally designed to prioritize recall—detecting even subtle tool anomalies over avoiding false alarms. The model effectively generalized across different tools without retraining, confirming its applicability as a unified anomaly detection module. These findings demonstrate strong potential for real-time predictive maintenance and cost-efficient smart manufacturing, especially in small and medium-sized enterprises.
본문·목차
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UCI(KEPA) : I410-151-26-02-094793221
