In the context of industrial big data,it is important to utilize multiple sensors deployed in complex equip-ment 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-di-mensional 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 trans-formation 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.
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
Key words
remaining useful life prediction/high-dimensional kernel estimation/feature fusion/relative density/ker-nel diffeomorphism transformation
维普
万方数据