LVDACNN: A Lightweight Variable Dependency Aware CNN for Remaining Useful Life Prediction

In recent years, deep learning technologies have shown significant advancements and have been widely adopted in remaining useful life (RUL) prediction approaches. However, the existing approaches primarily focus on learning the relationship between measured aging data of different time steps, neglecting the information contained within individual variables. Furthermore, most current approaches require a high computational cost to achieve accurate prediction. To address these limitations, this article proposes a novel deep learning model called lightweight variable dependency aware convolution neural network (LVDACNN) to analyze the measured aging data and make RUL prediction. The model consists of a data processing part and several LVDACNN blocks. The data processing part is designed to reduce the noise interference. Besides, an embedding scheme in the data processing part is proposed to enhance the variable relationship information; the designed LVDACNN block utilizes the time information in each variable and variable relationship to achieve accurate RUL prediction with low computation cost. Besides, to reduce the irrelevant information introduced by variable relationship learning, a channel attention mechanism is proposed. The LVDACNN model is evaluated using two public benchmark datasets for RUL prediction: the C-MAPSS dataset and the N-CMAPSS dataset. The experimental results demonstrate its superior performance compared to the state-of-the-art methods.