关键词: 剩余寿命预测, 高维核估计, 特征融合, 相对密度, 核微分同胚变换

Abstract: In the context of industrial big data,it is important to utilize multiple sensors deployed in complex equipment to obtain condition monitoring data for remaining useful life prediction to ensure the reliability and safety of the system.A relative density high-dimensional kernel estimation remaining useful life prediction method based on multi-sensor data fusion was proposed to fuse multi-sensor monitoring data more effectively.On the basis of one-dimensional kernel density estimation,a nonparametric high-dimensional kernel estimation features fusion model based on multi-sensor data fusion was established.The idea of k-nearest-neighbor relative density was introduced into the selection of the window width of high-dimensional kernel estimation,which could adaptively select a more reasonable window width according to the sparsity of data.A relative density high-dimensional kernel diffeomorphism transformation method was established using a spatial mapping approach to solve the boundary shift problem of high-dimensional kernel estimation in prediction,which could improve prediction accuracy.The accuracy and effectiveness of the proposed method were verified through experimental analysis of the aero-engine dataset and the gearbox dataset.

Key words: remaining useful life prediction, high-dimensional kernel estimation, feature fusion, relative density, kernel diffeomorphism transformation