Research of Wear Prediction of Gear System of Petrochemical Equipment based on Improved Grey Time Series Combination Model

He Zhaorong; Sun Zhiwei; Xuan Zhengnan; Duan Zhihong

doi:10.16578/j.issn.1004.2539.2017.01.011

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Research of Wear Prediction of Gear System of Petrochemical Equipment based on Improved Grey Time Series Combination Model

更新时间：2022-10-20

- Research of Wear Prediction of Gear System of Petrochemical Equipment based on Improved Grey Time Series Combination Model
- Vol. 41, Issue 1, Pages: 49-53(2017)
- 作者机构：
  
  1. 广东石油化工学院机电工程学院
  2. 广东石油化工学院广东省石化装备故障诊断重点实验室
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2017.01.011
  CLC：
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[1]何照荣,孙志伟,宣征南,段志宏.改进灰色时序模型的石化装备齿轮系统磨损预测研究[J].机械传动,2017,41(01):49-53+81.

He Zhaorong, Sun Zhiwei, Xuan Zhengnan, et al. Research of Wear Prediction of Gear System of Petrochemical Equipment based on Improved Grey Time Series Combination Model. [J]. 41(1):49-53(2017)
[1]何照荣,孙志伟,宣征南,段志宏.改进灰色时序模型的石化装备齿轮系统磨损预测研究[J].机械传动,2017,41(01):49-53+81. DOI： 10.16578/j.issn.1004.2539.2017.01.011.

He Zhaorong, Sun Zhiwei, Xuan Zhengnan, et al. Research of Wear Prediction of Gear System of Petrochemical Equipment based on Improved Grey Time Series Combination Model. [J]. 41(1):49-53(2017) DOI： 10.16578/j.issn.1004.2539.2017.01.011.

摘要

重载齿轮系统在大型石化工业生产装置中应用广泛,由于石化装置长周期运行的特点,且装置运行工况恶劣,齿轮系统磨损严重,对其磨损状态的监测和预测分析可及时掌握齿轮系统运行状态,确保装置的安全可靠运行。灰色系统模型可预测齿轮系统磨损量的变化,而时间序列模型可对磨损量预测数据的残差序列进行分析,将两者联立建立新的预测模型,达到综合调整和修正预测数据的目的。然后,改进灰色模型权重系数μ,以预测值和原始值的离差平方和作为考察指标,确定最佳权重系数μ,opt,提高灰色时序预测精度。通过某石化企业在用装置的重载齿轮系统铁谱监测数据作为样本,检验改进灰色时序预测模型效果,结果表明该预测模型的效果优于传统灰色时序预测模型。

Abstract

The heavy load gear system is widely used in large petrochemical industrial equipment. Because of the bad running conditions,the wear condition of the gear system is severe. According to the wear status of monitoring and forecasting analysis,the running condition of the gear system can be grasped,and to ensure the safe and reliable operation of the industrial units. The changing tendency of the wear quantity of the gear system can be represented by the grey prediction method,and the residual series of the predicted value can be analyzed by the time series method. The two kinds of methods can be combined to adjust and correct the predicted value.Then,the weight coefficient μ of the grey prediction model is improved. The sum of deviation square of the predicted value and the original value are set for investigation target to determine the optimum weight coefficientμopt,and the predicted precision is improved. The quantitative values of ferrography of the using heavy load gear system is analyzed as an example to test the effect of the improved grey time series combination model. The result shows that the improved grey time series combination model is superior to the traditional one.

关键词

油液分析灰色时序模型齿轮磨损铁谱

Keywords

Oil analysisGrey time series combination modelGear wearFerrography

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