Open Access

ARTICLE

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

by Kuosheng Jiang¹, Chengrui Han¹, Yasheng Chang^2,3,*

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

* Corresponding Author: Yasheng Chang. Email:

(This article belongs to the Special Issue: Computer-Aided Uncertainty Modeling and Reliability Evaluation for Complex Engineering Structures)

Computer Modeling in Engineering & Sciences 2024, 139(3), 3329-3352. https://doi.org/10.32604/cmes.2024.046729

Received 12 October 2023; Accepted 28 December 2023; Issue published 11 March 2024

Abstract

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 theory multi-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 from multiple 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.

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Keywords

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