Fault Diagnosis Method of High-pressure Common Rail System Based on EEMD-SVM

doi:10.12382/bgxb.2021.0155

Abstract

Abstract: When the high-pressure common rail system for diesel engine is running， the rail pressure fluctuation signal fluctuates greatly and has obvious nonlinear characteristics， which makes the fault diagnosis more difficult. For the problem that the state parameters of rail pressure signal in high-pressure common rail system are difficult to extract and identify， a fault diagnosis method based on ensemble empirical mode decomposition (EEMD)-support vector machine (SVM) is proposed. The rail pressure signal is decomposed into a series of eigenmode functions by EEMD， and the eigenvalues in the eigenmode functions are extracted using the feature extraction criterion determined by the zero-crossing rate curve. The extracted eigenvalues are input into SVM for fault type diagnosis. The rail pressure signal is obtained through AMESim software simulation experiment， and seven different eigenvalue selection methods are compared. Finally， the energy eigenvalue is selected to construct the eigenvalue vector for identification， and the diagnosis results are analyzed to verify the correctness and accuracy of the proposed method. The results show that the proposed EEMD-SVM-based fault diagnosis method for high-pressure common rail system can be used to identify six different operating states， with the average fault diagnostic accuracy rate of 96.11%.

Key words: high-pressurecommonrailsystem, faultdiagnosis, ensembleempiricalmodedecomposition, supportvectormachine

CLC Number:

TK428

LI Liangyu， SU Tiexiong， MA Fukang， WU Xiaojun， XU Chunlong. Fault Diagnosis Method of High-pressure Common Rail System Based on EEMD-SVM[J]. Acta Armamentarii, 2022, 43(5): 992-1001.

References

［1］樊高瞻，周俊，朱昆莉.基于改进形态-小波阈值降噪的轴承复合故障声学诊断［J］.振动与冲击，2020，39(12):221-226，288.
FAN G Z，ZHOU J，ZHU K L.An improved morphological-wavelet threshold de-noising method based acoustic diagnosis for bearing composite faults［J］.Journal of Vibration and Shock，2020，39(12): 221-226，288.（in Chinese）
［2］张健，张子阳，起雪梅，等.基于EMD和VPMCD的滚动轴承故障诊断［J］.机械设计，2019，36(增刊2):91-94.
ZHANG J，ZHANG Z Y，QI X M，et al. Fault diagnosis of rolling bearing based on EMD and VPMCD［J］.Journal of Mechane Design，2019，36(S2):91-94.(in Chinese)
［3］赵荣珍，张琛，邓林峰.基于EEMD与模糊信息熵的旋转机械故障诊断方法［J］.兰州理工大学学报，2020，46(3):39-44.
ZHAO R Z，ZHANG C，DENG L F. Fault diagnosis method of rotating machinery based on EEMD and fuzzy information entropy［J］.Journal of Lanzhou University of Technology，2020，46(3):39-44. (in Chinese)
［4］柴艳有.基于核学习理论的船舶柴油机故障诊断研究［D］. 哈尔滨：哈尔滨工程大学，2012.
CHAI Y Y.Research on fault diagnosis of marine diesel engine based on kernel-based learning theory［D］.Harbin: Harbin Engineering University，2012.(in Chinese)
［5］蒋佳炜，胡以怀，柯赟，等.基于小波包特征提取和模糊熵特征选择的柴油机故障分析［J］.振动与冲击，2020，39(4):273-277，298.
JIANG J W，HU Y H，KE Y，et al.Fault analysis of diesel engines based on wavelet packet energy spectrum feature extraction and fuzzy entropy feature selection［J］. Journal of Vibration and Shock，2020，39(4):273-277，298.(in Chinese)
［6］孟庆宇.基于EEMD-SVM的自动扶梯主驱动轴轴承故障诊断方法研究［J］.机械与电子，2020，38(5):51-53，58.
MENG Q Y. Research on fault diagnosis method for main drive shaft bearing of escalator based on EEMD-SVM［J］. Machinery and Electronics，2020，38(5):51-53，58.(in Chinese)
［7］郑小霞，周国旺，任浩翰，等.基于变分模态分解和排列熵的滚动轴承故障诊断［J］.振动与冲击，2017，36(22):22-28.
ZHENG X X，ZHOU G W，REN H H，et al.A rolling bearing fault diagnosis method based on variational mode decomposition and permutation entropy［J］.Journal of Vibration and Shock，2017，36(22): 22-28.(in Chinese)
［8］郭伟超，赵怀山，李成，等.基于小波包能量谱与主成分分析的轴承故障特征增强诊断方法［J］.兵工学报，2019，40(11):2370-2377.
GUO W C，ZHAO H S，LI C，et al.Fault feature enhancement method for rolling bearing fault diagnosis based on wavelet packet energy spectrum and principal component analysis［J］.Acta Armamentarii，2019，40(11): 2370-2377.(in Chinese)
［9］李志农，胡志峰，毛清华，等.非线性调频模态分解-同步提取变换方法及其在滚动轴承故障诊断中的应用［J］.兵工学报，2021，42(6):1324-1330.
LI Z N，HU Z F，MAO Q H，et al.Variational nonlinear chirp mode decomposition-synchroextracting transform method and its application in fault diagnosis of rolling bearings［J］.Acta Armamentarii，2021，42(6):1324-1330.(in Chinese)
［10］李志农，朱明.基于变分模态分解的机械故障诊断方法研究［J］.兵工学报，2017，38(3):593-599.
LI Z N，ZHU M.Research on mechanical fault diagnosis method based on variational mode decomposition［J］.Acta Armamentarii，2017，38(3): 593-599.(in Chinese)
［11］ BOSER B E，GUYON I M，VAPNIK V N.A training algorithm for optimal margin classifiers［C］∥Proceedings of the 5th Annual Workshop on Computational Learning Theory. Pittsburgh，PA，US：ACM，1992：144-152.
［12］ HOLLANDJ H. Adaptation in natural and artificial systems: an introductory analysis with applications to biology，control，and artificial intelligence［M］.Cambridge，MA，US:MIT Press，1992.
［13］李海涛，邓少贵，王跃祥，等.基于经验模态分解的核磁共振去噪方法研究［J］.西南石油大学学报(自然科学版)，2020，42(3):51-59.
LI H T，DENG S G，WANG Y X，et al. Research on NMR denoising method based on empirical mode decomposition［J］.Journal of Southwest Petroleum University (Science & Technology Edition)，2020，42(3):51-59.(in Chinese)
［14］李卿.EMD时频分析方法的研究与应用［D］.武汉：华中师范大学，2008.
LI Q.Research and application of EMD time-frequency analysis method［D］.Wuhan:Central China Normal University，2008.(in Chinese)
［15］ ZHANG J，TIAN J，WEN T，et al.Deep fault diagnosis for rotating machinery with scarce labeled samples［J］. Chinese Journal of Electronics，2020，29(4):693-704.
［16］ FERNNDEZ-DELGADO M，CERNADAS E，BARRO S，et al.Do we need hundreds of classifiers to solve real world classification problems［J］. The Journal of Machine Learning Research，2014，15(1):3133-3181.
［17］ CARUANA R，KARAMPATZIAKIS N， YESSENALINA A. An empirical evaluation of supervised learning in high dimensions［C］∥Proceedings of the 25th International Conference on Machine Learning.New York，NY，US: Association for Computing Machinery，2008.
［18］ WANG G，WANG T Y，ZHENG H T，et al.Man vs. machine: practical adversarial detection of malicious crowdsourcing workers［C］∥Proceedings of the 23rd USENIX Security Symposium.San Diego，CA，US: USENIX Association，2014：239-254.
［19］孔安生，王洪澄，李国正.神经网络集成与支持向量机在多值分类问题上的比较研究［J］.计算机工程与应用，2005，41(1): 46-47，119.
KONG A S，WANG H C，LI G Z. Comparison study on neural network ensemble and support vector machines for multiple classification problems［J］.Computer Engineering and Applications，2005，41(1):46-47，119.(in Chinese)
［20］武哲，张强，黄华蒙，等.基于多尺度排列熵的复合行星齿轮故障诊断研究［J］.机械设计与制造，2020(9):182-186.
WU Z，ZHANG Q，HUANG H M，et al. Research on fault diagnosis of compound planetary gears based on dynamic model and multiscale permutation entropy［J］. Mechanical Design and Manufacture，2020(9):182-186.(in Chinese)
［21］ LEI Y G，LIN J，HE Z J，et al. A review on empirical mode decomposition in fault diagnosis of rotating machinery［J］.Mechanical Systems and Signal Processing，2013，35(1/2):108-126.
［22］李如强，陈进，伍星.基于奇异值分解、模糊聚类和粗糙集理论的旋转机械故障诊断［J］.振动与冲击，2005，24(4):46-49.
LI R Q，CHEN J，WU X.Fault diagnosis of rotating machinery based on SVD，FCM and RST［J］.Journal of Vibration and Shock，2005，24(4):46-49. (in Chinese)
［23］林煜凯，张会林.基于EEMD和ICS-SVM的齿轮箱故障诊断研究［J］.电力科学与工程，2020，36(10):56-62.
LIN Y K，ZHANG H L. Study on fault diagnosis of gearbox based on EEMD and ICS-SVM［J］.Electric Power Science and Engineering，2020，36(10):56-62.(in Chinese)

[1]	WANG Jiahao, NA Wenbo, LIU Zhiwei, ZAN Qi, WANG Zheng. Sensor Fault Diagnosis Method of Double Closed Loop Ratio Control System [J]. Acta Armamentarii, 2024, 45(4): 1252-1263.
[2]	QIU Jiarong, ZENG Pengfei, SHAO Weiping, ZHAO Lijun, HAO Yongping. PSO-LSSVM-Based Ammunition Assembly Quality Prediction Method [J]. Acta Armamentarii, 2022, 43(9): 2379-2387.
[3]	DUAN Hao， CHEN Hui， ZHAI Zhaoyang， HAN Yu， MA Fanhua， CUI Yahui. Performance Prediction of Hydrogen-enriched Compressed Natural Gas Engine Based on Support Vector Machine [J]. Acta Armamentarii, 2022, 43(5): 1002-1011.
[4]	YANG Jianxin， LAN Xiaoping， FENG Yadong， YANG Yiming， GUO Zhiming. An Ammunition Quality Evaluation Method Based on Least Squares Support Vector Machine [J]. Acta Armamentarii, 2022, 43(5): 1012-1022.
[5]	XU Yuxin, GU Chumei, CAO Jianjun, XU Jinyong, WEI Zhihu. Specific Emitter Identification of Twin Radiation Sources Based on Parallel Classifier Ensemble Learning Using Ant ColonyFeature Selection [J]. Acta Armamentarii, 2022, 43(12): 3132-3141.
[6]	LI Zedong, LI Zhinong, TAO Junyong, MAO Qinghua, ZHANG Xuhui. Fault Diagnosis for Aero-engine Rolling Bearings Based on an Attention Augmented Convolutional Neural Network with FeatureFusion [J]. Acta Armamentarii, 2022, 43(12): 3228-3239.
[7]	TIAN Heng, XU Rongbin, JIANG Yanhong, ZHANG Wenhu, DENG Si'er. Fault Diagnosis Strategy for Multi-valued Attribute System Based on a Discrete Particle Swarm Optimization Algorithm [J]. Acta Armamentarii, 2022, 43(12): 3240-3246.
[8]	MI Junxia, YU Huilong, XI Junqiang. Prediction of Driver's Lane Changing Behavior Based on MLP-SVM [J]. Acta Armamentarii, 2022, 43(12): 3020-3029.
[9]	LI Wei, HAN Yinlong, SUN Xiaojing. Incipient Fault Diagnosis Method of Vehicle Power Supply Based on Feature Optimization and Deep Learning [J]. Acta Armamentarii, 2022, 43(11): 2935-2944.
[10]	LI Zhinong, HU Zhifeng, MAO Qinghua, ZHANG Xuhui, TAO Junyong. Variational Nonlinear Chirp Mode Decomposition-synchroextracting Transform Method and Its Application in Fault Diagnosisof Rolling Bearing [J]. Acta Armamentarii, 2021, 42(6): 1324-1330.
[11]	ZHU Min， XU Aiqiang， XU Qing， LI Ruifeng. Fault Diagnosis of Analog Circuits Based on Improved Multilayer Kernel Extreme Learning Machine [J]. Acta Armamentarii, 2021, 42(2): 356-369.
[12]	MA Runbo， DONG Lihong， WANG Haidou. Prediction of Contact Fatigue Life of Thermal Sprayed Coating Based on Support Vector Machine [J]. Acta Armamentarii, 2021, 42(12): 2743-2752.
[13]	LEI Zhenshuo, LIU Songtao, WEN Zhenming, GE Yang. A Real-time Discrimination Method for Operating State of Terminal Guidance Radar in Complex Electromagnetic Environment [J]. Acta Armamentarii, 2021, 42(1): 83-90.
[14]	WANG Cheng， WU Yan， YANG Tingfei. Improving the Detection Accuracy of Wake Targets by Improved One-class Support Vector Machine [J]. Acta Armamentarii, 2020, 41(9): 1887-1893.
[15]	L Guojun， CAO Jianjun， ZHENG Qibin， CHANG Chen， WENG Nianfeng， PENG Cong. Detection of Similar Duplicate Records Based on OCSVM and Multi-objective Ant Colony Optimization [J]. Acta Armamentarii, 2020, 41(2): 324-331.