Remaining useful life prediction of relative density high-dimensional kernel estimation for systems based on multi-sensor data fusion
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.
remaining useful life predictionhigh-dimensional kernel estimationfeature fusionrelative densityker-nel diffeomorphism transformation
万方数据
维普
