다변량 시계열 데이터를 이용한 잔여 유효 수명 예측을 위한 셀프 어텐션 기반 2D CNN 모델

초록

Purpose: This research aims to introduce a novel a methodology for predicting the Remaining Useful Life (RUL) using multivariate time series data. Methods: The proposed RUL prediction methodology comprises of the following steps: 1) Reorganizing the multivariate time series data to enhance the correlation between different time series datasets; 2) Streamlining various time series data into a single pixel utilizing 2D convolutional layers; 3) Emphasizing the substantial correlation among different time series using a self-attention layer; 4) Estimating the RUL with Bi-LSTM and fully connected layers. Results: In comparison with existing deep learning models utilizing the identical test datasets, the proposed model exhibits greater performance in RUL prediction. A detailed analysis reveals the model’s merits in terms of data reorganization alongside the application of 2D CNN and multi-head self attention layers in the RUL prediction. Conclusion: The proposed model provides more accurate RUL estimation results relative to pre-existing models using multivariate datasets obtained from multiple sensors, showing promising potential for its use in real-world applications.

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