Jixie chuandong (Jan 2017)
Research of Wear Prediction of Gear System of Petrochemical Equipment based on Improved Grey Time Series Combination Model
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.
