Predicting leakage in a piston pump using HP filter and ARIMA-GARCH model

doi:10.11975/j.issn.1002-6819.2022.10.008

	Predicting leakage in a piston pump using HP filter and ARIMA-GARCH model
	Chen, Le 1; Gao, Wenke1 ; Ji, Hong1 ; Zhang, Lei 2
	2022-05-01
发表期刊	Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering
ISSN	1002-6819
卷号	38 期号:10 页码:61-67
摘要	A piston pump has been widely used in large agricultural equipment, and walking machinery. However, the leakage can often be induced by the performance degradation with the increase of cumulative running time, even threat to the volumetric efficiency and operation safety of the equipment. It is a high demand for the accurate and rapid prediction of the performance degradation state in the piston pump for better working reliability. In this study, the leakage was selected as the characteristic indicator to quantitatively analyze the performance degradation of the piston pump. A Hodrick-Proscott (HP) filtering was first utilized to divide the leakage volume of the piston pump into the long-term trend data and short-term fluctuation data in the modeling. A correlation function graph of long-term trend term data was drawn to observe the effective lag order, and then the Akaike information criterion (AIC) criterion was used to select the optimal order. The autoregressive integrated moving average model (ARIMA(16, 3, 0)) was established to verify the data characteristics of the residual sequence using the statistical analysis. After that, a generalized autoregressive conditional heteroscedasticity (GARCH (1,1)) model was established to combine with the data characteristics of nonlinear and heteroscedasticity of long-term trend data. As such, an HP-ARIMA-GARCH model was established for the trend item data to predict the leakage of the piston pump. The residual sequence was approximately normal distribution after testing, fully meeting the requirements of the model. The leakage prediction was performed on the same pump in different time periods and different pumps in the same time period. The accuracy of the proposed model was then determined to compare with the linear ARIMA model and the nonlinear self-excitable threshold autoregression (SETAR) model. The mean absolute error (MAE), mean square error (MSE), and the mean relative error (MRE) of the HP-ARIMA model were significantly reduced in all prediction periods, compared with the traditional ARIMA model, where the maximum average relative error was 2.42%. Therefore, it infers that the HP filtering can be widely expected to effectively extract the long-term trend of leakage data, and then filter out the noise interference for the lower prediction error of the time series model. Furthermore, there was a relatively smaller error in the nonlinear time series model for the higher prediction accuracy, compared with the traditional linear model. Nevertheless, there was a varying prediction of the improved model in the prediction accuracy. The reason was the inconsistent leakage of the piston pumps during the different periods and the rapid fluctuation in the short time during operation. More importantly, the error of unfiltered data was less than that of the filtered, indicating that the nonlinear model performed better than the linear one. The highest prediction accuracy was achieved in the HP-ARIMA-GARCH model, where the average relative error was within 1.5%. Therefore, the improved time series model was more accurate than before, particularly suitable for the leakage prediction of the piston pump. Above all, the finding can provide a theoretical reference to quantitatively predict the performance degradation of piston pump, especially for the decision-making on the equipment reliability and operational safety. The long-term prediction accuracy can be further improved in the time series model in the future. Besides, the fluctuation data by the HP filter can also be expected for the higher prediction accuracy of leakage, considering the valuable information of the time series model, rather than the errors during modeling. © 2022 Chinese Society of Agricultural Engineering. All rights reserved.
关键词	Errors Mean square error Nonlinear analysis Normal distribution Pistons Regression analysis ARIMA models ARIMA-GARCH Data characteristics Garch modelling Hodrick-proscott filter Leakage prediction Long-term trend Performance degradation Prediction accuracy Times series models
DOI	10.11975/j.issn.1002-6819.2022.10.008
收录类别	EI
语种	中文
出版者	Chinese Society of Agricultural Engineering
EI入藏号	20224713158386
EI主题词	Forecasting
EI分类号	612.1.1 Internal Combustion Engine Components ; 922.1 Probability Theory ; 922.2 Mathematical Statistics
引用统计	无
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/160653
专题	能源与动力工程学院科学技术处（军民融合领导小组办公室）
作者单位	1.School of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou; 730050, China; 2.School of Mechanical and Electrical Engineering, Xuzhou University of Technology, Xuzhou; 221018, China
第一作者单位	新能源学院
第一作者的第一单位	新能源学院
推荐引用方式 GB/T 7714	Chen, Le,Gao, Wenke,Ji, Hong,et al. Predicting leakage in a piston pump using HP filter and ARIMA-GARCH model[J]. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering,2022,38(10):61-67.
APA	Chen, Le,Gao, Wenke,Ji, Hong,&Zhang, Lei.(2022).Predicting leakage in a piston pump using HP filter and ARIMA-GARCH model.Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering,38(10),61-67.
MLA	Chen, Le,et al."Predicting leakage in a piston pump using HP filter and ARIMA-GARCH model".Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering 38.10(2022):61-67.