柴油增压发动机故障诊断参数数据的可信度研究

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英文篇名：Study of the Reliability of Diesel Turbocharged Engine Fault Diagnosis Parameter Data 作者：陈雷 ; 杨丽娟 英文作者：CHEN lei;YANG Lijuan;School of Computer Science and Engineering,Xi'an Technological University;Department of Electronics,Xi'an University of Technological Information; 关键词：可信度 ; 故障诊断 ; 发动机 ; 经验迭代 英文关键词：reliability;;fault diagnosis;;engine;;empirical iteration 中文刊名：西安工业大学学报 英文刊名：Journal of Xi’an Technological University 机构：西安工业大学计算机科学与工程学院;西安工业大学北方信息工程学院电子系; 出版日期：2019-08-01 出版单位：西安工业大学学报 年：2019 期：04 语种：中文; 页：107-113 页数：7 CN：61-1458/N ISSN：1673-9965 分类号：TK428

摘要

针对传统故障诊断方法未考虑到参数数据可信度以及历史故障诊断信息导致的误报率高的问题,文中利用故障诊断信息提出了经验迭代算法,确定了每种诊断参数权重数值,在诊断之前对数据进行可信度加权使其中的野值剔除或者减轻其权重,将各个参数的数据融合得出概率值并判断发动机是否出现故障。通过对添加可信度计算的故障诊断与不添加可信度的诊断进行对比,结果表明,故障数据在没有异常的情况下两种方法基本持平;在出现异常的情况下,文中方法较之传统方法,准确率明显提高。
        Traditional fault diagnosis methods have the limitations of low reliability of parameter data and high false alarm rate caused by historical fault diagnosis information.The paper presents an empirical iteration algorithm based on fault diagnosis information with the weight values of each diagnostic parameter determined.Before diagnosis,the reliability of the data is weighted to eliminate outliers or reduce their weight.The probability values are obtained by fusing the data of each parameter,on the basis of which the engine failure is judged.The comparison between the fault diagnosis methods with and without adding credibility shows that both methods are basically the same in the absence of abnormal fault data;when fault data are abnormal,the accuracy of the proposed method is significantly higher than that of the traditional methods.

引文

[1]钟毅.船艇柴油发动机故障诊断系统研究与设计[D].南京:南京理工大学,2014.ZHONG Yi.Research and Design of Fault Diagnosis System for Diesel Engine of Ship Boat[D].Nanjing:Nanjing University of Science and Technology,2014.(in Chinese)
    [2]马进锐.航空发动机故障诊断与振动预测技术研究[D].西安:西北工业大学,2015.MA Jinrui.Research on Fault Diagnosis and Vibration Prediction of Aeroengine[M].Xi’an:Northwestern Polytechnical University,2015.(in Chinese)
    [3]马善伟,乐正伟,吕健,等.柴油机故障诊断技术综述[J].上海第二工业大学学报,2008,25(2):122.MA Shanwei,LE Zhengwei,LYU Jian,et al.Summarization of Fault Diagnostic Technology for Diesel[J].Journal of Shanghai Second Polytechnic University,2008,25(2):122.(in Chinese)
    [4]张剑.基于振动信号的柴油机神经网络故障诊断技术研究[D].昆明:昆明理工大学,2012.ZHANG Jian.Research on Fault Diagnosis of Diesel Engine Based on Vibration Signal[D].Kunming:Kunming University of Science and Technology,2012.(in Chinese)
    [5]李宏坤,马孝江,王珍.基于多征兆信息融合理论的柴油机故障诊断[J].农业机械学报,2004,35(1):121.LI Hongkun,MA Xiaojiang,WANG Zhen.Diesel Engine Fault Diagnosis Based on Multi-symptom Information Fusion[J].Transactions of The Chinese Society of Agricultural,2004,35(1):121.(in Chinese)
    [6]杨剑锋,周宇,侯涛,等.基于传感器特征可信度的多信息融合模态研究[J].传感器与微系统,2013,32(2):50.YANG Jianfeng,ZHOU Yu,HOU Tao,et al.Study on Multi-Information Fusion Model Based on Feature Credibility of Sensor[J].Transducer and Microsystem Technologies,2013,32(2):50.(in Chinese)
    [7]胡丽芳,关欣,何友.基于可信度的证据融合方法[J].信号处理,2010,26(1):17.HU Lifang,GUAN Xin,HE You.Evidence Fusion Method Based on Reliability[J].Signal Processing,2010,26(1):17.(in Chinese)
    [8]张多林,潘泉,张洪才,等.一种基于信息源可信度的证据组合新方法[J].系统工程与电子技术,2008(7):1210.ZHANG Duolin,PAN Quan,ZHANG Hongcai,et al.Combination Rule of Evidence Theory Based on Credibility of Sensor[J].Systems Engineering and Electronics,2008(7):1210.(in Chinese)
    [9]高晓清.基于多传感器信息融合的柴油发动机故障诊断研究[D].太原:中北大学,2008.GAO Xiaoqing.Research on Fault Diagnosis of Diesel Engine Based on Multi-sensor Information Fusion[D].Taiyuan:North University of China,2008.(in Chinese)
    [10]鲁峰.航空发动机故障诊断的融合技术研究[D].南京:南京航空航天大学,2009.LU Feng.Research on Fusion Technology of Aircraft Engine Fault Diagnosis[D].Nanjing:Nanjing University of Aeronautics and Astronautics,2009.(in Chinese)
    [11]韩慧勇.基于多源信息融合的柴油机故障诊断硏究[D].太原:中北大学,2012.HAN Huiyong.A Diesel Engine Fault Diagnosis based on Multi-Source Information Fusion[D].Taiyuan:North University of China,2012.(in Chinese)