公开(公告)号：US20240289610A1

公开(公告)日：2024-08-29

申请号：US18214881

申请日：2023-06-27

Applicant: Beijing Institute of Technology

Inventor： Gang WANG ,  Hongjie CAO ,  Jian SUN ,  Minggang GAN ,  Jie CHEN

IPC: G06N3/08 ,  G06F17/14 ,  G06F17/16 ,  G06N3/0442 ,  G06N3/0464 ,  H03H17/02

CPC classification number: G06N3/08 ,  G06F17/142 ,  G06F17/16 ,  G06N3/0442 ,  G06N3/0464 ,  H03H17/0257

Abstract: Disclosed is a hybrid data- and model-driven method for predicting remaining useful life of a mechanical component. The method of the present disclosure uses an extended Kalman filter to calibrate parameters of an exponential random model, automatically learns input embedded position information by means of an adaptive encoding layer of a hybrid driven prediction model, and then models a mapping relation between input data and the remaining useful life by means of a multi-head attention mechanism. The present disclosure retains both accuracy of a model-based method and a generalization capability of a data-driven method in combination with the calibrated exponential random model and a multi-head attention neural network structure, can improve accuracy of predicting the remaining useful life of the mechanical component, and has great significance for use of the hybrid data- and model-driven method in the field of intelligent manufacturing and health management of mechanical apparatuses.