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A Study on the Health Index Based on Degradation Patterns in Time Series Data Using ProphetNet Model KCI 등재
ProphetNet 모델을 활용한 시계열 데이터의 열화 패턴 기반 Health Index 연구
pp.123-138
The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.
1.1 연구 배경 및 필요성
1.2 연구절차 및 구성
2. 관련 문헌 연구
3. 이론적 배경
3.1 시계열 데이터 분석
3.2 패턴 분류
3.3 Health Index(HI)
4. 패턴 기반 HI 수립 프로세스 개발
4.1 패턴 기반 HI 수립 프로세스 제안
4.2 변수 선택
4.3 군집 분석 기반 패턴 분류
4.4 현재 상태 기반 Health Index 개발
5. 분석 및 평가
5.1 데이터 전처리
5.2 분석 결과
6. 결론 및 추후 연구
Acknowledgement
