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基于KPLS的滚动轴承装配质量预测

  

  1. （1. 江苏南瑞恒驰电气装备有限公司 江苏 无锡 214000；2.中国电力科学研究院 江苏 无锡 214000；3.江南大学 机械工程学院 江苏 无锡 214122）
  • 出版日期:2016-08-30 发布日期:2016-10-08
  • 作者简介: 万方华(1968-)男，湖北省人，高级工程师，硕士,主要研究方向为高压电器产品的设计与制造.
  • 基金资助:

     国家自然科学基金资助项目（51107053）

Rolling Bearing Assembly Quality Forecasting Based on Kernel Partial Least Squares

  1. (1. Wuxi Hengchi Electric Apparatus Manufacturing Co., Ltd, Wuxi 214000, China; 2. State Grid Electric Power Research Institute, Wuxi 214000, China;
    3.School of Mechanical Engineering，Jiangnan University, Wuxi 214122, China）
  • Online:2016-08-30 Published:2016-10-08

摘要：

 轴承的装配质量在机械运行过程中起到十分关键的作用。为了预测轴承的装配质量，建立了能够准确表征轴承特性与零件几何要素间关系的数学模型，对轴承产品装配质量进行预测。结果表明采用核偏最小二乘回归法(kernel partial least squares,KPLS)对轴承的装配质量进行预测，克服了实际生产中非线性因素对预测模型的不利影响，具有很好的预测精度，为轴承的精准制造提供了帮助，具有十分重要的意义。

关键词:  , 轴承, 装配质量, 预测, 回归

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