基于局部线性模型树模型的柴油机进气系统漏气和堵塞故障诊断

王英敏;崔涛;张付军;董天普

兵工学报 ›› 2017, Vol. 38 ›› Issue (8) : 1457-1468.

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  • 主管：中国科学技术协会

    主编：许毅达

    ISSN 1000-1093

    　

  • 主办：中国兵工学会

    出版：《兵工学报》 编辑部

    CN 11-2176/TJ

《兵工学报》是我国国防科技领域最具影响力的学术期刊之一，创刊于1979年，目前被国内所有重要数据库收录，其综合评价指标近年来一直名列兵工技术类科技期刊的首位；《兵工学报》目前还被国际上著名的检索机构美国......
兵工学报 ›› 2017, Vol. 38 ›› Issue (8) : 1457-1468. DOI: 10.3969/j.issn.1000-1093.2017.08.001
论文

基于局部线性模型树模型的柴油机进气系统漏气和堵塞故障诊断

  • 王英敏1,2， 崔涛1， 张付军1， 董天普1
作者信息 +

Fault Diagnosis of Intake System of Diesel Engine Based on LOLIMOT

  • WANG Ying-min1,2， CUI Tao1， ZHANG Fu-jun1， DONG Tian-pu1
Author information +
文章历史 +

摘要

柴油机进气系统漏气和堵塞等性能退化型故障将会导致排放恶化和经济性下降。针对进气系统具有较强的非线性，难以建立精确的数学模型问题，提出基于局部线性模型树（LOLIMOT）模型的故障诊断方法，为在线故障诊断提供一种思路。提取进气压力波的一次谐波信号幅值与相位、充量系数为故障特征，采用 LOLIMOT方法对充量系数、进气压力波动幅值与相位信号建立基于发动机转速和进气密度的参考模型；采用奇偶方程生成3个残差信号，计算各个残差信号的阈值；分析残差信号和故障类型的映射关系。结果表明：所建进气压力波动幅值LOLIMOT模型、进气压力波动相位LOLIMOT模型、充量系数模型验证数据与模型仿真数据具有较高线性相关度；采用充量系数、进气压力波幅值与相位为故障特征值建立的LOLIMOT模型生成残差信号的故障诊断方法，可诊断进气系统漏气故障和中冷器堵塞故障。

Abstract

Leakage and blocking of intake system of diesel engine may lead to the deterioration of the emissionsand fuel economy. An accurate model of intake system is difficult to be established since it is a strong nonlinear system. Thus, a fault diagnosis method based on local linear model tree (LOLIMOT) is proposed for on-line fault diagnosis.The amplitude and phase of intake pressure fluctuation signal and volumetric efficiency are selected as the fault characteristic parameters. LOLIMOT is used to establish the reference models of the parameters. The models are regarded as a function of engine speed and intake density.Three residual signals are generated by using parity equation for calculating their threshold values. The mapping relationship between the residual signal and the fault type is analyzed. Experimental results show that the proposed models give good prediction of fault characteristic parameters, the proposed method can be to diagnose theleakage of engine intake system and the blocking of intercooler. Key

关键词

动力机械工程 / 进气系统 / 性能退化型故障 / 局部线性模型树 / 故障诊断

Key words

powermachineryengineering / intakesystem / degradationfault / locallinearmodeltree / faultdiagnosis

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王英敏， 崔涛， 张付军， 董天普. 基于局部线性模型树模型的柴油机进气系统漏气和堵塞故障诊断. 兵工学报. 2017, 38(8): 1457-1468 https://doi.org/10.3969/j.issn.1000-1093.2017.08.001
WANG Ying-min， CUI Tao， ZHANG Fu-jun， DONG Tian-pu. Fault Diagnosis of Intake System of Diesel Engine Based on LOLIMOT. Acta Armamentarii. 2017, 38(8): 1457-1468 https://doi.org/10.3969/j.issn.1000-1093.2017.08.001

基金

武器装备预先研究项目（3030020121104）

参考文献



［1］SidorowA, Isermann R, Cianflone F, et al. Model based fault detection of the air and exhaust path of diesel engines including turbocharger models［C］∥SAE 2011 World Congress & Exhibition. Detroit, MI, US: SAE, 2011.
［2］周兴利.电控柴油机故障智能诊断研究［D］. 上海：上海交通大学,2007:3-4.
ZHOU Xing-li. Study of intelligence diagnosis for electronic controlled diesel engine faults［D］. Shanghai: Shanghai Jiao Tong University,2007:3-4. (in Chinese)
［3］PourbabaeeB, Meskin N, Khorasani K. Sensor fault detection, isolation,and identification using multiple-model-based hybrid Kalman filter for gas turbine engines［J］. IEEE Transactions on Control Systems Technology, 2016, 24(4):1184-1200.
［4］荣吉利. 基于模型的航天器在轨传感器故障诊断方法［J］. 兵工学报, 2002, 23(2):242-245.
RONG Ji-li. A model-based onboard fault diagnosis for spacecraft sensors［J］. Acta Armamentarii, 2002, 23(2):242-245. (in Chinese)
［5］孙云龙,刘波澜,崔涛，等.基于模型的柴油机进气系统故障诊断方法研究［J］.车用发动机,2013(3):84-87.
SUN Yun-long, LIU Bo-lan, CUI Tao, et al. Model-based fault diagnosis method of diesel engine intake system［J］. Vehicle Engine, 2013(3):84-87. (in Chinese)
［6］Rissoni G, Min P. Detection of sensor failures in automotive engines［J］.IEEE Transactions on Vehicular Technology, 1991, 40(2):487-500.
［7］Hamad A, Yu D, Gomm J B, et al. Radial basis function neural network in fault detection of automotive engines［J］. International Journal of Engineering Science & Technology,2010, 2(10):1-8.
［8］Cook W R, Kepes F, Joseleau-Petit D, et al. Dynamic fault detection and isolation for automotive engine air path by independent neural network model［J］. International Journal of Engine Research, 2012, 15(1):87-100.
［9］ NybergM, Perkovic A. Model based diagnosis of leaks in the air-intake system of an SI-engine［C］∥SAE International Congress and Exposition. Detroit, MI, US: SAE, 1998.

［10］VinsonneauJ A F, Shields D N, King P J, et al. Improved SI engine modelling techniques with application to fault detection［C］∥International Conference on Control Applications. Glasgow, Scotland: IEEE, 2002:719-724.
［11］Svrd C, Nyberg M, Frisk E, et al. Automotive engine FDI by application of an automated model-based and data-driven design methodology［J］. Control Engineering Practice, 2013, 21(4):455-472.
［12］Duyar A, Eldem V, Merrill W. Fault detection and diagnosis in propulsion systems: a fault parameter estimation approach［J］. AIAA Journal of Guidance, Control and Dynamics, 1994, 17(1): 104-108.
［13］PetchinathanG, Valarmathi K, Devaraj D, et al. Local linear model tree and neuro-fuzzy system for modelling and control of an experimental pH neutralization process［J］. Brazilian Journal of Chemical Engineering, 2014, 31(2):483-495.
［14］Lucas C, Hajian A, Zomorrodian H, et al. Depth estimation of cavities from microgravity data using a new approach: the local linear model tree (LOLIMOT)［J］. Near Surface Geophysics, 2012, 10(3):221-234.
［15］王新宇, 何超, 李加强,等. 基于局部线性模型树的高压共轨柴油机排放模型［J］. 车用发动机, 2015(4):16-20.
WANG Xin-yu,HE Chao,LI Jia-qiang,et al. Emission model of high-pressure commom rail diesel engine based on local linear model tree［J］. Vehicle Engine, 2015(4): 16-20.(in Chinese)
［16］Kolewe B, Haghani A, Beckmann R, et al. Gaussian mixture regression and local linear network model for data-driven estimation of air mass［J］. Iet Control Theory & Applications, 2015, 9(7): 1083-1092.
［17］逄红. 内燃机神经网络建模与优化控制方法研究［D］. 天津：天津大学, 2003:41-47.
FENG Hong. Research on neural network modeling and optimization control method of internal combustion engine［D］. Tianjin: Tianjin University, 2003:41-47.(in Chinese)
［18］Sequenz H. Emission model structures for an implementation on engine control units［C］∥18th IFAC World Congress. Milano,Italy: IFAC , 2011:11851-11856.
［19］Zakharov A, Tikkala V M, Jms-Jounela S L. Fault detection and diagnosis approach based on nonlinear parity equations and its application to leakages and blockages in the drying section of a board machine［J］. Journal of Process Control, 2013, 23(9):1380-1393.
［20］韩恺, 吴滔滔, 赵长禄,等. 基于DOE和神经网络的增压柴油机实时模型建模方法研究［J］. 内燃机工程, 2014,35(1):57-62.
HAN Kai, WU Tao-tao, ZHAO Chang-lu, et al. Development of real-time capable turbocharged diesel engine model based on DOE and neural network［J］. Chinese Internal Combustion Engine Enginerring,2014, 35(1):57-62. (in Chinese)
［21］Li Q C, Li D D, Chen Q Y. Prediction of pre-oxidation efficiency of refractory gold concentrate by ozone in ferric sulfate solution using artificial neural networks［J］. Transactions of Nonferrous Metals Society of China,2011,21(2):418-422.
［22］Pan L I, Li F G, Cao J, et al. Constitutive equations of 1060 pure aluminum based on modified double multiple nonlinear regression model［J］. Transactions of Nonferrous Metals Society of China, 2016, 26(4):1079-1095.
［23］杜巍, 刘福水, 李志杰. 增压柴油机进气压力波动规律的试验研究［J］. 内燃机工程, 2008,29(3):37-40.
DU Wei, LIU Fu-shui, LI Zhi-jie. Experimental study on fluctuation of intake pressure in turbocharged diesel engine［J］. Chinese Internal Combustion Engine Engineering, 2008, 29(3):37-40.(in Chinese)
［24］Kimmich F, Schwarte A, Isermann R. Fault detection for modern diesel engines using signal- and process model-based methods［J］. Control Engineering Practice, 2005, 13(2):189-203.






第38卷
第8期2017年8月兵工学报ACTA
ARMAMENTARIIVol.38No.8Aug.2017

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