Time Series-Based Sensor Selection and Lightweight Neural Architecture Search for RUL Estimation in Future Industry 4.0

Abstract

Industry 4.0 is the current development trend in manufacturing technology. Regarding prognostic and health management (PHM) systems, a major task is to predict the remaining useful life (RUL) of a machine. To have precise RUL information, massive sensors should be involved, which leads to the dramatic cost of sensor network construction. Therefore, many sensor selection methods have been proposed to remove the redundant sensors under a certain constraint of RUL estimation. However, those approaches do not consider time-series sensing data, which is an intrinsic feature of mechanical signals. Therefore, the current research suffers from sensor under-killing problems due to the pessimistic consideration. To solve this problem, this work first considers the time-series sensing data to propose an integrated group-based valuable sensor selection algorithm by using the Least Absolute Shrinkage and Selection Operator (LASSO) property. In addition, to reduce the computing and hardware overhead, we further propose a heuristic neural architecture search (NAS) method to reduce the number of neurons in each neural network for further RUL estimation. To verify the proposed approaches, we use the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and utilize the PHM score to evaluate the performance of RUL estimation. Compared with the conventional methods, the proposed sensor selection method can reduce the PHM score by 9% to 99% with fewer sensors. On the other hand, the proposed NAS method can reduce the number of neurons by 86% to 89% and reduce the PHM score by 17% to 79% compared with the traditional NAS approaches.