Research on Optimal Preload Method of Controllable Rolling Bearing Based on Multisensor Fusion  

作　　者：Kuosheng Jiang Chengrui Han Yasheng Chang 

机构地区：[1]School of Mechanical and Electrical Engineering,Anhui University of Science and Technology,Huainan,232001,China [2]School of Optical and Electronic Information,Suzhou City University&Suzhou Key Laboratory of Biophotonics,Suzhou,215104,China [3]State Key Laboratory for Manufacturing Systems Engineering,Xi’an Jiaotong University,Xi’an,710054,China

出　　处：《Computer Modeling in Engineering & Sciences》2024年第6期3329-3352,共24页工程与科学中的计算机建模（英文）

基　　金：supported by:The Key Project of National Natural Science Foundation of China(U21A20125);The Open Project of State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines(SKLMRDPC21KF03);The National Key Research and Development Program of China(2020YFB1314203,2020YFB1314103);The Open Project of Key Laboratory of Conveyance and Equipment(KLCE2021-05);The Science and Technology Research Project of Jiangxi Provincial Department of Education(GJJ210639);The Supply and Demand Linking Employment Education Project of the Ministry of Education(20220100621);The Open Project of State Key Laboratory for Manufacturing Systems Engineering(sklms2023009);The Suzhou Basic Research Project(SJC2023003).

摘　　要：Angular contact ball bearings have been widely used in machine tool spindles,and the bearing preload plays an important role in the performance of the spindle.In order to solve the problems of the traditional optimal preload prediction method limited by actual conditions and uncertainties,a roller bearing preload test method based on the improved D-S evidence theorymulti-sensor fusion method was proposed.First,a novel controllable preload system is proposed and evaluated.Subsequently,multiple sensors are employed to collect data on the bearing parameters during preload application.Finally,a multisensor fusion algorithm is used to make predictions,and a neural network is used to optimize the fitting of the preload data.The limitations of conventional preload testing methods are identified,and the integration of complementary information frommultiple sensors is used to achieve accurate predictions,offering valuable insights into the optimal preload force.Experimental results demonstrate that the multi-sensor fusion approach outperforms traditional methods in accurately measuring the optimal preload for rolling bearings.

关 键 词：MULTI-SENSOR information fusion neural network preload force 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]