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 ...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.展开更多
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)+5 种基金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).
文摘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.
