전력 부하 분석을 통한 절삭 공정 이상탐지

• 영문명: Anomaly Detection of Machining Process based on Power Load Analysis
• 발행기관: 한국산업경영시스템학회
• 저자명: 육준홍 배성문
• 간행물 정보: 『산업경영시스템학회지』제46권 제4호, 173~180쪽, 전체 8쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2023.12.31

영문 초록

Smart factory companies are installing various sensors in production facilities and collecting field data. However, there are relatively few companies that actively utilize collected data, academic research using field data is actively underway. This study seeks to develop a model that detects anomalies in the process by analyzing spindle power data from a company that processes shafts used in automobile throttle valves. Since the data collected during machining processing is time series data, the model was developed through unsupervised learning by applying the Holt Winters technique and various deep learning algorithms such as RNN, LSTM, GRU, BiRNN, BiLSTM, and BiGRU. To evaluate each model, the difference between predicted and actual values was compared using MSE and RMSE. The BiLSTM model showed the optimal results based on RMSE. In order to diagnose abnormalities in the developed model, the critical point was set using statistical techniques in consultation with experts in the field and verified. By collecting and preprocessing real-world data and developing a model, this study serves as a case study of utilizing time-series data in small and medium-sized enterprises.

목차

1. 서론

2. 절삭 공정 데이터 분석

3. 데이터 전처리

4. 이상 탐지 모델 학습 및 검증

5. 결론 및 추후 연구

키워드

Machining Process Power Load Anomaly Detection LSTM BiLSTM