Convolutional Autoencoder-Based Method for Predicting Faults of Cyber-Physical Systems Based on the Extraction of a Semantic State Vector

Modern industrial equipment is a cyber-physical system (CPS) consisting of physical production components and digital controls. Lowering maintenance costs and increasing availability is important to improve its efficiency. Modern methods, based on solving event prediction problem, in particular, prediction of remaining useful life (RUL) are used as a crucial step in a framework of the reliability-centered maintenance to increase efficiency. But modern methods of RUL forecasting fall short when dealing with real-world scenarios, where CPS are described by multidimensional continuous high frequency data with working cycles with variable duration. To overcome this problem, we propose a new method for faults prediction, which is based on extraction of semantic state vectors (SSV) from working cycles of equipment. To implement SSV extraction, a new method, based on convolutional autoencoder and extraction of hidden state is proposed. In this method, working cycles are detected in input data stream, then they are converted to images, on which autoencoder is trained. The output of an intermediate layer of autoencoder is extracted and processed into SSV. SSVs are then combined into a time series, on which RUL is forecasted. After optimization of hyperparameters the proposed method shows following results: RMSE = 1.799, MAE = 1,374, which is significantly more accurate than existing methods. Therefore, SSV extraction is a viable technique for forecasting RUL.