Statistical Analysis Of Oil Consumption Of Hydraulic Excavator

With the rapid development of construction machinery,the number of excavators is increasing rapidly.At the same time,the fuel consumption is also increasing.The energy saving and emission reduction of excavators are also more and more concerned by the society.Therefore,the analysis of excavator fuel consumption and the establishment of fuel consumption model become one of the important links of energy conservation and environmental protection.The working environment of excavators is complex,and enterprises are also setting appropriate working modes according to different working conditions to reduce the fuel consumption of excavators.Therefore,there are many external and internal factors that affect the fuel consumption of excavators,and they are closely related.These factors bring great difficulty to the analysis of fuel consumption of excavators.Based on the above problems,the main work of this dissertation is as follows:Firstly,the collected data of hydraulic excavator are cleaned and processed to meet the requirements of subsequent analysis and establishment of fuel consumption model.Then,the main parameters of three types of hydraulic excavators under two different working modes are analyzed to find out the variables that have strong correlation with the oil consumption of hydraulic excavators.Then,the main features of all variables are extracted by Lasso(Least absolute shrinkage and selection operator)method to further analyze the significant relationship between variables and oil consumption.Finally,the oil consumption model of BP(Back Propagation)neural network and support vector regression is constructed based on the data of hydraulic excavator in P and e working mode.According to the significant variables extracted by lasso method,the index system is established,and then the fuel consumption model is established with the influence parameters of each working mode as the input and the fuel consumption of hydraulic excavator as the output.The performance of the model is verified with the data of verification set,and then the performance of the two models is compared.The simulation results suggests that the different working modes have an important impact on the oil consumption of hydraulic excavators,and the significant variables of different types of excavators are different.At the same time,the oil consumption model established by BP neural network performs better than that established by support vector regression.So using BP neural network for the actual oil consumption estimation of hydraulic excavator can provide certain technical guidance,can effectively predict the oil consumption of hydraulic excavator,realize energy saving and emission reduction and green development.