Rolling Bearing Assembly Quality Forecasting Based on Kernel Partial Least Squares

Abstract

Abstract:

The assembly quality of bearing plays a vital role in the mechanical operation process. In order to predict the quality of the bearing assembly, a mathematical model of accurately formulating relations between bearing characteristics and geometric components is proposed to predict assembly quality of bearings, which is very important to quality control of bearings. Using the kernel partial least square method to predict bearing assembly quality can overcome the adverse effects of the nonlinear factors. Research shows that the method results in a better accuracy and is helpful to bearing precision manufacturing.

Key words: bearing, assembly quality, predict, regression

WAN Fanghua1，WANG Xiaoqi2, ZHANG Hong3，DING Shiyong3，CHEN Jianguo1，WANG Tongde1，PAN Wenbin1. Rolling Bearing Assembly Quality Forecasting Based on Kernel Partial Least Squares[J]. Industrial Engineering Journal .

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