Research on Fault Diagnosis Strategy of Diesel Particulate Filter Coupled with Particle Sensor

TANG Dong; LI Tianxiang; SHI Shengyao

doi:10.7652/xjtuxb202309010

您当前的位置：

首页 >

文章列表页 >

Research on Fault Diagnosis Strategy of Diesel Particulate Filter Coupled with Particle Sensor

更新时间：2025-07-09

- Research on Fault Diagnosis Strategy of Diesel Particulate Filter Coupled with Particle Sensor
- Vol. 57, Issue 9, Pages: 87-97(2023)
- 作者机构：
  
  江苏大学汽车与交通工程学院,江苏,镇江,212013
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202309010
  CLC： TK421.5
- Online First：10 September 2023，
  
  Published：2023
- 稿件说明：
移动端阅览
TANG Dong, LI Tianxiang, SHI Shengyao. Research on Fault Diagnosis Strategy of Diesel Particulate Filter Coupled with Particle Sensor[J]. 2023, 57(9): 87-97.
DOI：

TANG Dong, LI Tianxiang, SHI Shengyao. Research on Fault Diagnosis Strategy of Diesel Particulate Filter Coupled with Particle Sensor[J]. 2023, 57(9): 87-97. DOI： 10.7652/xjtuxb202309010.

摘要

为实时监测柴油机颗粒捕集器(DPF)工作状态以建立相应的故障诊断策略

利用GT-POWER软件建立DPF碳烟捕集模型

通过发动机排放试验标定DPF的渗透率; 模拟DPF不同程度的堵塞故障和破损故障

对压降和捕集效率受故障程度的影响进行敏感性分析; 搭建试验台架

制作达到法规限制的破损件和堵塞件

研究故障件在全球统一瞬态循环工况下的压降与传感器信号边界

提出了压差传感器与颗粒物传感器耦合的故障诊断方法。试验结果表明:DPF压降随堵塞程度增加呈指数型攀升

捕集效率增加较平缓; DPF压降和捕集效率随破损程度增加逐级递减

高转速工况下破损程度达到20%时捕集效率下降至50%

任何工况下破损程度达到50%时DPF完全失效。此外

破损故障试验确定了DPF破损故障的压降边界下限为0.1 kPa

传感器信号边界上限为10 nA; 堵塞试验确定了碳载量7 g/L时DPF堵塞故障的压降边界上限为1.75 kPa

传感器信号边界下限为15 nA。

Abstract

In order to monitor the working state of the diesel particulate filter(DPF)in real time

it is necessary to establish the corresponding fault diagnosis strategy. First

a DPF soot capture model was established by using GT-POWER software

and the penetration of DPF was calibrated through engine emission tests. Then

different degrees of blockage faults and breakage faults of DPF were simulated and pressure drop and capture efficiency were selected for research to analyze their sensitivity to fault degree. Finally

the pressure drop and sensor signal boundary of the faulty parts under world harmonized transient conditions were studied by building test benches and making broken and blocked parts that met the legal limits and a fault diagnosis method of coupling the differential pressure sensor and the particle sensor was proposed. Research shows that the DPF pressure drop increases exponentially with the increase of blockage degree

while the increase of capture efficiency is relatively gentle. However

the DPF pressure drop and capture efficiency gradually decrease with the increase of the breakage degree. Under the condition of high speed

the capture efficiency decreases to 50% when the breakage degree reaches 20%

and when the breakage degree increases to 50%

the DPF completely fails regardless of working condition. In addition

the breakage fault test determines that the lower limit of the pressure drop boundary of the DPF breakage fault is 0.1 kPa and the upper limit of the sensor signal boundary is 10 nA; the blockage test determines that the upper limit of the pressure drop boundary of the DPF blockage fault with a carbon load of 7 g/L is 1.75 kPa

and the lower limit of the sensor signal boundary is 15 nA.

关键词

Keywords

references

陈仁祥, 胡超超, 孙健, 等. 基于深度学习的DPF故障诊断及应用 [J]. 重庆理工大学学报(自然科学), 2022, 36(8): 126-133.

CHEN Renxiang, HU Chaochao, SUN Jian, et al. DPF fault diagnosis and application based on deep learning [J]. Journal of Chongqing University of Technology(Natural Science), 2022, 36(8): 126-133.

刘军恒, 孙平, 刘源, 等. PODE掺混比对高压共轨柴油机颗粒物物理特性的影响 [J]. 西安交通大学学报, 2017, 51(12): 104-111.

LIU Junheng, SUN Ping, LIU Yuan, et al. Effects of PODE blending ratio on the physical characteristics of particulate matters for high-pressure common-rail diesel engines [J]. Journal of Xi'an Jiaotong University, 2017, 51(12): 104-111.

崔应欣, 蔡忆昔, 施蕴曦, 等. 残余灰分对低温等离子体再生颗粒物捕集器的影响 [J]. 西安交通大学学报, 2018, 52(11): 150-155.

CUI Yingxin, CAI Yixi, SHI Yunxi, et al. Effects of residual ash on diesel particulate filter regeneration by non-thermal plasma technology [J]. Journal of Xi'an Jiaotong University, 2018, 52(11): 150-155.

SHI Yunxi, LU Yirui, CAI Yixi, et al. Evolution of particulate matter deposited in the DPF channel during low-temperature regeneration by non-thermal plasma [J]. Fuel, 2022, 318: 123552.

CHEN Chao, YAO Anren, YAO Chunde, et al. Experimental study of the active and passive regeneration procedures of a diesel particulate filter in a diesel methanol dual fuel engine [J]. Fuel, 2020, 264: 116801.

刘理凡, 王忠, 刘帅, 等. 柴油机排气颗粒吸附与结构特征试验研究 [J]. 西安交通大学学报, 2020, 54(3): 134-142.

LIU Lifan, WANG Zhong, LIU Shuai, et al. Experimental study on adsorption and structural feature of diesel engine exhaust particles [J]. Journal of Xi'an Jiaotong University, 2020, 54(3): 134-142.

SHI Yunxi, ZHOU Yin, LI Zhengsheng, et al. Effect of temperature control conditions on DPF regeneration by nonthermal plasma [J]. Chemosphere, 2022, 302: 134787.

戴金池, 庞海龙, 俞妍, 等. 柴油车DPF系统的OBD故障诊断策略研究 [J]. 车用发动机, 2020(1): 80-85.

DAI Jinchi, PANG Hailong, YU Yan, et al. OBD fault diagnosis strategy for DPF of diesel vehicle [J]. Vehicle Engine, 2020(1): 80-85.

POLVERINO P, ELEFANTE S, ARSIE I, et al. Design of a model-based diagnostic algorithm for diesel particulate filter fault detection and isolation [J]. AIP Conference Proceedings, 2019, 2191(1): 020131.

YANG Kun, FOX J T, HUNSICKER R. Characterizing diesel particulate filter failure during commercial fleet use due to pinholes, melting, cracking, and fouling [J]. Emission Control Science and Technology, 2016, 2(3): 145-155.

YOUSSEF B. A novel approach for diesel particulate filter diagnostic using resistive sensor [C]//2018 International Conference on Advanced Systems and Electric Technologies(IC_ASET). Piscataway, NJ, USA: IEEE, 2018: 162-167.

KONTSES D, GEIVANIDIS S, SAMARAS Z. Challenges and error propagation of PM sensor-based DPF diagnostics [EB/OL].(2018-04-01)[2023-03-01].https://www.researchgate.net/publication/326106722_Challenges_and_error_propagation_of_PM_sensor-based_DPF_diagnostics.

SAPPOK A, RAGALLER P, HERMAN A, et al. Continuous particulate filter state of health monitoring using radio frequency sensing [C]//WCX World Congress Experience.Warrendale, PA, USA: SAE International, 2018: 2018-01-1260.

姚广涛, 郭子荣. 基于压力信号谱分析的DPF故障诊断策略研究 [J]. 汽车工程, 2015, 37(7): 831-834, 830.

YAO Guangtao, GUO Zirong. A research on DPF fault diagnosis strategy based on pressure signal spectrum analysis [J]. Automotive Engineering, 2015, 37(7): 831-834, 830.

姚广涛, 伍恒, 张卫锋, 等. 基于温度数据相关分析的DPF故障诊断策略 [J]. 内燃机工程, 2016, 37(3): 36-41.

YAO Guangtao, WU Heng, ZHANG Weifeng, et al. DPF fault diagnosis strategy based on the analysis of temperature related data [J]. Chinese Internal Combustion Engine Engineering, 2016, 37(3): 36-41.

LIU Shuangxi, LÜ Ming. Fault diagnosis of the blocking diesel particulate filter based on spectral analysis [J]. Processes, 2019, 7(12): 943.

汤东, 吴宇哲, 陈烈. 柴油机漏电流式颗粒物传感器可靠性分析与优化 [J]. 内燃机工程, 2016, 37(5): 70-73, 79.

TANG Dong, WU Yuzhe, CHEN Lie. Reliability analysis and optimization of current-leak PM sensor for diesel engine [J]. Chinese Internal Combustion Engine Engineering, 2016, 37(5): 70-73, 79.

李楠, 汤东, 陈烈. 柴油机车载诊断系统新型颗粒物传感器的研究 [J]. 仪表技术与传感器, 2013(6): 1-3, 26.

LI Nan, TANG Dong, CHEN Lie. Research on new particulate matter sensor for diesel on-board diagnostics [J]. Instrument Technique and Sensor,2013(6): 1-3, 26.

TANG Dong, ZHAO Ruonan, WANG Songhua, et al. The simulation and experimental research of particulate matter sensor on diesel engine with diesel particulate filter [J]. Sensors and Actuators: A Physical, 2017, 259: 160-170.

王欣伟, 程德新, 张军, 等. 柴油机DPF系统失效故障特征分析 [J]. 兵器装备工程学报, 2022, 43(2): 229-234, 273.

WANG Xinwei, CHENG Dexin, ZHANG Jun, et al. Analysis on failure characteristics of DPF system of diesel engine [J]. Journal of Ordnance Equipment Engineering, 2022, 43(2): 229-234, 273.

XIAO Ge, LI Bo, TIAN Hua, et al. Numerical study on flow and pressure drop characteristics of a novel type asymmetric wall-flow diesel particulate filter [J]. Fuel, 2020, 267: 117148.

ORIHUELA M P, CHACARTEGUI R, GÓMEZ-MARTÍN A, et al. Performance trends in wall-flow diesel particulate filters: comparative analysis of their filtration efficiency and pressure drop [J]. Journal of Cleaner Production, 2020, 260: 120863.

SHAO Shushan, CHENG Junnan, ZHANG Bo, et al. Effects of a DOC+DPF system on emission characteristics of China Π engineering vehicle diesel engine and influence factors of trapping efficiency of PM for DOC+DPF system [J]. Energy Sources: Part A Recovery, Utilization, and Environmental Effects, 2019, 41(5): 527-541.

李振, 姚广涛, 伍恒, 等. 柴油机后处理DPF堵塞故障仿真与优化研究 [J]. 内燃机与配件, 2018(21): 5-8.

LI Zhen, YAO Guangtao, WU Heng, et al. Simulation and optimization of DPF blocking fault with GT-POWER [J]. Internal Combustion Engine Parts, 2018(21): 5-8.

朱亚永, 赵昌普, 王耀辉, 等. 柴油机DPF流场压降及微粒沉积特性数值模拟 [J]. 内燃机学报, 2017, 35(6): 538-547.

ZHU Yayong, ZHAO Changpu, WANG Yaohui, et al. Numerical simulation of pressure-drop and soot particle accumulation performance of a diesel engine DPF [J]. Transactions of CSICE, 2017, 35(6): 538-547.

OU Juan, MENG Zhongwei, HU Yizhang, et al. Experimental investigation on the variation characteristics of soot layer thickness and pressure drop during DPF/CDPF active regeneration [J]. Chemical Engineering Science, 2021, 241: 116682.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Construction and Application of Multi-Agent Large Language Model for Power Transformer Fault Diagnosis

Dual-Wavelet Dictionary Based Feature Extraction Method for Compound Faults in Gearboxes

Study on the Interpretability of One-Dimensional Convolutional Neural Networks in Mechanical Fault Feature Extraction

Application of Mayfly Algorithm Optimized Dual-Channel Network in Gearbox Fault Diagnosis

Application of Interpretable Convolutional Neural Network Enabled by Bi-Damped Wavelets in Bearing Fault Diagnosis

Related Author

LIN Jinshan

LI Yuan

FANG Qixuan

LIU Zhihao

LI Chunpeng

ZHANG Guanjun

WANG Xiangdong

LIU Qiang

Related Institution

西安交通大学电气工程学院，710049，西安School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, China

College of Hydraulic & Environmental Engineering, China Three Gorges University

China Yangtze Power Co., Ltd.

College of Mechanical and Power Engineering, China Three Gorges University

Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance, China Three Gorges University

⁰