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Data fusion for fault diagnosis using multi-class Support Vector Machines 被引量:1
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作者 胡中辉 蔡云泽 +1 位作者 李远贵 许晓鸣 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2005年第10期1030-1039,共10页
Multi-source multi-class classification methods based on multi-class Support Vector Machines and data fusion strategies are proposed in this paper. The centralized and distributed fusion schemes are applied to combine... Multi-source multi-class classification methods based on multi-class Support Vector Machines and data fusion strategies are proposed in this paper. The centralized and distributed fusion schemes are applied to combine information from several data sources. In the centralized scheme, all information from several data sources is centralized to construct an input space. Then a multi-class Support Vector Machine classifier is trained. In the distributed schemes, the individual data sources are proc-essed separately and modelled by using the multi-class Support Vector Machine. Then new data fusion strategies are proposed to combine the information from the individual multi-class Support Vector Machine models. Our proposed fusion strategies take into account that an Support Vector Machine (SVM) classifier achieves classification by finding the optimal classification hyperplane with maximal margin. The proposed methods are applied for fault diagnosis of a diesel engine. The experimental results showed that almost all the proposed approaches can largely improve the diagnostic accuracy. The robustness of diagnosis is also improved because of the implementation of data fusion strategies. The proposed methods can also be applied in other fields. 展开更多
关键词 Data fusion fault diagnosis Multi-class classification Multi-class Support Vector machines Diesel engine
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Aircraft Engine Gas Path Fault Diagnosis Based on Hybrid PSO-TWSVM 被引量:6
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作者 Du Yanbin Xiao Lingfei +1 位作者 Chen Yusheng Ding Runze 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2018年第2期334-342,共9页
Twin support vector machine(TWSVM)is a new development of support vector machine(SVM)algorithm.It has the smaller computation scale and the stronger ability to cope with unbalanced problems.In this paper,TWSVM is intr... Twin support vector machine(TWSVM)is a new development of support vector machine(SVM)algorithm.It has the smaller computation scale and the stronger ability to cope with unbalanced problems.In this paper,TWSVM is introduced into aircraft engine gas path fault diagnosis.The generalization capacity of Gauss kernel function usually used in TWSVM is relatively weak.So a mixed kernel function is used to improve performance to ensure that the TWSVM algorithm can better balance a strong generalization ability and a good learning ability.Experimental results prove that the cross validation training accuracy of TWSVM using the mixed kernel function averagely increases 2%.Grid search is usually applied in parameter optimization of TWSVM,but it heavily depends on experience.Therefore,the hybrid particle swarm algorithm is introduced.It can intelligently and rapidly find the global optimum.Experiments prove that its training accuracy is better than that of the classical particle swarm algorithm by 5%. 展开更多
关键词 aircraft engines fault diagnosis TWIN support VECTOR machine (TWSVM) hybrid PARTICLE SWARM optimization (HPSO) algorithm mixed KERNEL function
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Fault Identification and Health Monitoring of Gas Turbine Engines Using Hybrid Machine Learning-based Strategies 被引量:1
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作者 Yan-yan Shen Khashayar Khorasani 《风机技术》 2022年第1期71-80,共10页
Ahealth monitoring scheme is developed in this work by using hybrid machine learning strategies to iden-tify the fault severity and assess the health status of the aircraft gas turbine engine that is subject to compon... Ahealth monitoring scheme is developed in this work by using hybrid machine learning strategies to iden-tify the fault severity and assess the health status of the aircraft gas turbine engine that is subject to component degrada-tions that are caused by fouling and erosion.The proposed hybrid framework involves integrating both supervised recur-rent neural networks and unsupervised self-organizing maps methodologies,where the former is developed to extract ef-fective features that can be associated with the engine health condition and the latter is constructed for fault severity modeling and tracking of each considered degradation mode.Advantages of our proposed methodology are that it ac-complishes fault identification and health monitoring objectives by only discovering inherent health information that are available in the system I/O data at each operating point.The effectiveness of our approach is validated and justified with engine data under various degradation modes in compressors and turbines. 展开更多
关键词 Gas Turbine Engines Health Monitoring fault Identification Self-organizing Maps machine Learn-ing Recurrent Neural Networks
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Fault Identification of Internal Combustion Engine based on Support Vector Machine and Fuzzy Neural Network
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作者 CHEN Decheng HE Xinyu 《International Journal of Plant Engineering and Management》 2022年第3期144-157,共14页
The internal combustion engine is the main power source of current large⁃scale machinery and equipment.Overhaul and maintenance of its faults are important conditions for ensuring the safe and stable operation of mach... The internal combustion engine is the main power source of current large⁃scale machinery and equipment.Overhaul and maintenance of its faults are important conditions for ensuring the safe and stable operation of machinery and equipment,and the identification of faults is a prerequisite.Therefore,the fault identification of internal combustion engines is one of the important directions of current research.In order to further improve the accuracy of the fault recognition of internal combustion engines,this paper takes a certain type of internal combustion engine as the research object,and constructs a support vector machine and a fuzzy neural network fault recognition model.The binary tree multi⁃class classification algorithm is used to determine the priority,and then the fuzzy neural network is verified.The feasibility of the model is proved through experiments,which can quickly identify the failure of the internal combustion engine and improve the failure processing efficiency. 展开更多
关键词 internal combustion engine support vector machine fuzzy neural network fault recognition
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基于表面辐射声信号的柴油机进气及齿轮故障诊断
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作者 李斌 林杰威 +3 位作者 朱小龙 林耕毅 张益铭 张俊红 《排灌机械工程学报》 CSCD 北大核心 2024年第8期843-850,共8页
利用声振信号进行发动机故障诊断过程中,部分故障激励仅在发动机表面特定位置的振动中有较强响应,振动测点要求高,需要接触测量,部分场景难以实现.为此,提出了一种以表面辐射声为媒介、以自适应变分模态提取(adaptive variational mode ... 利用声振信号进行发动机故障诊断过程中,部分故障激励仅在发动机表面特定位置的振动中有较强响应,振动测点要求高,需要接触测量,部分场景难以实现.为此,提出了一种以表面辐射声为媒介、以自适应变分模态提取(adaptive variational mode extraction,AVME)进行预处理的柴油机进气故障和齿轮故障诊断方法.开展了某直列六缸重型柴油机的进气滤清器堵塞、气门间隙异常和正时齿轮损伤3类故障状态的台架试验,获取了不同故障程度下发动机表面辐射噪声.基于改进的AVME方法,实现噪声信号本征模函数(intrinsic mode function,IMF)的最优分解,通过计算IMF与原信号间的互相关系数,提取高相关IMF构成故障诊断输入.经预处理后,声信号故障特征得到有效增强,再输入到麻雀搜索算法优化支持向量机模型(support vector machine model optimized by sparrow search algorithm,SSA-SVM),进行特征参量和模型参数协同优化可以获得更好的诊断精度.试验验证表明,无需在半消声室测试,仅使用单通道声信号对3类11种程度的进气系统和齿轮故障进行诊断,前端噪声准确率最高(98.89%),顶部噪声准确率最低(88.78%);使用前、顶、后三通道噪声数据后,诊断精度可提升至99.57%.研究结论为基于声信号等非接触测量的发动机故障诊断提供了参考. 展开更多
关键词 柴油机 声信号 故障诊断 自适应变分模态提取 支持向量机
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基于粗糙集理论与PCA-APSO-SVM的沥青路面使用性能预测
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作者 李海莲 杨斯媛 +2 位作者 祁增涛 刘忠磊 李清华 《重庆交通大学学报(自然科学版)》 CAS CSCD 北大核心 2024年第8期10-17,共8页
针对传统沥青路面使用性能预测精度较低的问题,建立了基于粗糙集理论(rough set,RS)与主成分分析法(principal compoent analysis,PCA)-自适应粒子群算法(adaptive particle swarm optimization,APSO)-支持向量机(support vector machin... 针对传统沥青路面使用性能预测精度较低的问题,建立了基于粗糙集理论(rough set,RS)与主成分分析法(principal compoent analysis,PCA)-自适应粒子群算法(adaptive particle swarm optimization,APSO)-支持向量机(support vector machine,SVM)的沥青路面使用性能预测模型。基于沥青路面的时序指标与影响因素指标,建立了11个初始预测指标(包括前3年的路面使用性能、当量轴次、路龄、养护性质、坑槽率、修补率、年降水量、平均气温、日照时数);通过RS属性约减筛选出9个核心指标;利用PCA提取4个主成分,得到了基于4个主成分的数据集;将APSO引入到SVM中,对数据集进行训练,并优化了SVM模型参数;建立了路面使用性能的PCA-APSO-SVM预测模型,并以G6京藏高速甘肃境内某段道路为例,对路面使用性能进行预测。研究结果表明:PCA-APSO-SVM模型预测精度较PCA-PSO-SVM、APSO-SVM、PSO-SVM有较大提高,预测结果与实际情况更加符合,能为路面养护决策提供相关参考。 展开更多
关键词 道路工程 路面使用性能预测 粗糙集理论 主成分分析 粒子群算法 支持向量机
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矿井通风系统智能故障诊断MC-OCSVM模型
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作者 沈志远 杨镇隆 +1 位作者 焦莉 赵丹 《安全与环境学报》 CAS CSCD 北大核心 2024年第8期3126-3132,共7页
为解决矿井通风系统故障分支判识不准确的问题,引入单分类算法,构建了多个单分类支持向量机(One-Class Support Vector Machines, OCSVM)集成的通风系统故障诊断模型。模型采用统一超参数并设计了尺度统一公式以实现多个输出尺度的统一... 为解决矿井通风系统故障分支判识不准确的问题,引入单分类算法,构建了多个单分类支持向量机(One-Class Support Vector Machines, OCSVM)集成的通风系统故障诊断模型。模型采用统一超参数并设计了尺度统一公式以实现多个输出尺度的统一,将通风系统故障诊断问题转变为最大决策距离问题,建立仅需正常样本参与训练的通风系统故障诊断半监督学习模型,实现对矿井监测风速数据的有效利用。进行了KEEL公开数据集和东山煤矿生产矿井实例试验,结果表明,单分类集成模型能够解决多分类问题,与其他单分类集成模型相比,单分类支持向量机集成(Multi-Class One-Class SVM,MC-OCSVM)模型具有最佳的泛化性,所提模型能够快速准确地识别通风系统故障分支,故障诊断准确率达93.2%,单次故障诊断时间为1.2 s,具有较强的鲁棒性。研究工作是实现矿井通风智能化的基础,为通风系统故障诊断提供技术支撑。 展开更多
关键词 安全工程 矿井通风 智能算法 故障诊断 单分类集成 单分类支持向量机(OCSVM)
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一种基于TCN-LGBM的航空发动机气路故障诊断方法 被引量:1
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作者 吕卫民 孙晨峰 +2 位作者 任立坤 赵杰 李永强 《兵工学报》 EI CAS CSCD 北大核心 2024年第1期253-263,共11页
长时间工作在高温高压、强振动等恶劣气路环境下的航空发动机经常面临部件疲劳、腐蚀和性能退化的问题,且其故障诊断时序逻辑性不强、故障参数耦合较深等特点十分明显,为此提出一种基于时间卷积神经网络(Temporal Convolutional Network... 长时间工作在高温高压、强振动等恶劣气路环境下的航空发动机经常面临部件疲劳、腐蚀和性能退化的问题,且其故障诊断时序逻辑性不强、故障参数耦合较深等特点十分明显,为此提出一种基于时间卷积神经网络(Temporal Convolutional Network,TCN)和轻量级梯度提升机(Light Gradient Boosting Machine,LGBM)的航空发动机气路故障诊断方法。故障诊断分为故障特征提取和分类诊断两个过程:引入TCN框架,在保证故障数据训练时序逻辑的基础上,实现对远层历史信息和当前层信息的特征融合构建,融合通道注意力机制增强了高质量特征的权重;基于LGBM模型实现对特征的快速分类,利用贝叶斯方法实现对模型超参数的快速优化。以基于PROOSIS软件建模的某军用小涵道比涡扇发动机故障仿真数据为例,对6种故障模式进行诊断识别。仿真结果表明了所提方法的有效性;通过与其他模型对比体现了该方法的优越性。 展开更多
关键词 航空发动机 故障诊断 时间卷积神经网络 轻量级梯度提升机 注意力机制
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基于强化层次模糊熵的柴油机故障诊断方法
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作者 宋业栋 马光伟 +1 位作者 朱小龙 张俊红 《振动.测试与诊断》 EI CSCD 北大核心 2024年第4期814-820,834,共8页
针对多尺度模糊熵(multi-scale fuzzy entropy,简称MFE)算法中多尺度化过程采用滑动均值滤波器导致原始信号高频信息丢失的问题,提出强化层次模糊熵方法(enhanced hierarchical fuzzy entropy,简称EHFE),用于表征原始信号中富含的高低... 针对多尺度模糊熵(multi-scale fuzzy entropy,简称MFE)算法中多尺度化过程采用滑动均值滤波器导致原始信号高频信息丢失的问题,提出强化层次模糊熵方法(enhanced hierarchical fuzzy entropy,简称EHFE),用于表征原始信号中富含的高低频故障模式信息。结合萤火虫算法优化支持向量机(firefly algorithm optimized support vector machine,简称FAOSVM),提出一种基于EHFE和FAOSVM的柴油机故障诊断方法。柴油机试验数据对比分析表明:相比于现有方法,所提出方法能够充分表征柴油机故障信号富含的模式信息,并且能够有效识别柴油机正时齿轮故障,识别精度达到99.6%,在极小样本下也能达到较好的识别精度。 展开更多
关键词 强化层次模糊熵 柴油机 正时齿轮 故障诊断 萤火虫算法优化支持向量机
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基于EMATE和POA-ELM的声音信号故障诊断方法
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作者 徐浙君 王凯 +1 位作者 罗少杰 崔炳荣 《机电工程》 CAS 北大核心 2024年第6期956-968,共13页
常规的工程机械故障诊断方法一般需对振动信号进行分析,但采集振动信号时需要使振动传感器与工程机械相接触,在某些情况下工程机械表面不适合安装传感器,如设备的温度较高或者传感器的安装空间有限。针对这些问题,以声音信号作为故障诊... 常规的工程机械故障诊断方法一般需对振动信号进行分析,但采集振动信号时需要使振动传感器与工程机械相接触,在某些情况下工程机械表面不适合安装传感器,如设备的温度较高或者传感器的安装空间有限。针对这些问题,以声音信号作为故障诊断对象,提出了一种基于增强多尺度注意熵(EMATE)和鹈鹕优化算法优化极限学习机(POA-ELM)的工程机械故障诊断方法。首先,利用声音传感器采集了工程机械不同故障的声音信号,避免了振动传感器存在的接触式采集缺陷;然后,利用EMATE提取了声音信号中的故障信息,建立了表征工程机械不同故障状态的特征向量;接着,鉴于ELM的参数需要优化的问题,采用POA对ELM的关键参数进行了寻优,建立了参数自适应设置的ELM分类模型;最后,利用POA-ELM分类器对故障特征进行了辨识,实现了工程机械的故障识别,并利用往复压缩机和滚动轴承的声音信号数据集对基于EMATE-POA-ELM的故障诊断方法的有效性进行了验证。研究结果表明:将EMATE方法作为故障特征提取指标能够取得100%和99.23%的识别准确率,且特征提取的时间仅为53.88 s和172.47 s;与多尺度注意熵、复合多尺度注意熵、时移多尺度注意熵等指标相比,EMATE的平均故障识别准确率更高,并具有更好的综合性能。 展开更多
关键词 工程机械 往复压缩机 滚动轴承 故障数据集 增强多尺度注意熵 故障诊断 鹈鹕优化算法优化极限学习机
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基于LSTM-PSO-SVM的船用LNG双燃料发动机故障预测
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作者 姜峰 杨奕飞 《舰船科学技术》 北大核心 2024年第4期120-126,共7页
针对船用LNG双燃料发动机设备复杂,故障预测效率低、准确度差的问题,提出一种长短期记忆网络与改进粒子群优化算法优化支持向量机融合的预测模型。利用LSTM模型时间序列变化的能力对设备未来的运行状态进行预测,然后采用非线性自适应惯... 针对船用LNG双燃料发动机设备复杂,故障预测效率低、准确度差的问题,提出一种长短期记忆网络与改进粒子群优化算法优化支持向量机融合的预测模型。利用LSTM模型时间序列变化的能力对设备未来的运行状态进行预测,然后采用非线性自适应惯性权重改进PSO算法对SVM参数进行寻优,以提高其寻优能力和收敛速度;改进的LSTM-PSO-SVM融合模型可实现对设备故障状态的快速、准确预测。通过对某船用LNG双燃料发动机的故障预测仿真,结果表明上述模型具有更高的故障识别准确率和更快的识别速度,能够准确预测船用LNG双燃料发动机潜在故障。 展开更多
关键词 长短期记忆网络 改进粒子群优化 支持向量机 故障预测 LNG双燃料发动机
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基于传感信号采集的电控发动机振动故障监测方法
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作者 马晓 郑晅 柴艳娜 《传感技术学报》 CAS CSCD 北大核心 2024年第4期675-681,共7页
通过调理振动信号可以更高效地监测振动故障。为此,提出基于传感信号采集的电控发动机振动故障监测方法。首先,搭建电控发电机传感信号采集与处理架构,通过放大传感信号增益、滤波和转换信号模数的方式处理待监测信号,为提高监测准确性... 通过调理振动信号可以更高效地监测振动故障。为此,提出基于传感信号采集的电控发动机振动故障监测方法。首先,搭建电控发电机传感信号采集与处理架构,通过放大传感信号增益、滤波和转换信号模数的方式处理待监测信号,为提高监测准确性奠定可靠的数据基础。通过小波包分解与重构,获取信号的时域参数和小波能谱熵,并构建三维特征量。然后,利用“一对一”分解策略优化孪生支持向量机,构造多元分类器,使其更适用于振动故障监测这一多类别分类问题,再输入待监测信号的特征量,通过确定故障类别实现持续性监测。仿真结果表明:该方法训练耗时的最大值仅为897 ms,对于转子摩擦振动、不平衡振动等5种类型故障的监测准确率始终在97%以上,在缩减训练样本后准确率仍保持在90%以上。 展开更多
关键词 信号与信息处理 振动故障监测 传感信号采集 电控发动机 信号调理 信号转换 小波能谱熵 孪生支持向量机
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Misfire identification of automobile engines based on wavelet packet and extreme learning machine
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作者 GAO Yuan LI Yi-bo 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2017年第4期384-395,共12页
Due to non-stationary characteristics of the vibration signal acquired from cylinder head,a misfire fault diagnosis system of automobile engines based on correlation coefficient gained by wavelet packet and extreme le... Due to non-stationary characteristics of the vibration signal acquired from cylinder head,a misfire fault diagnosis system of automobile engines based on correlation coefficient gained by wavelet packet and extreme learning machine(ELM)is proposed.Firstly,the original signal is decomposed by wavelet packet,and correlation coefficients between the reconstructed signal of each sub-band and the original signal as well as the energy entropy of each sample are obtained.Then,the eigenvectors established by the correlation coefficients method and the energy entropy method fused with kurtosis are inputted to the four kinds of classifiers including BP neural network,KNN classifier,support vector machine and ELM respectively for training and testing.Experimental results show that the method proposed in this paper can effectively reflect the differences that the fault produces and identify the single-cylinder misfire accurately,which has the advantages of higher accuracy and shorter training time. 展开更多
关键词 automobile engine wavelet packet correlation coefficient extreme learning machine (ELM) misfire fault identification
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基于1 DCNN-GWO-SVM的柴油机喷油系统故障诊断方法研究
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作者 冯海波 毛玉欣 +3 位作者 孔祥鑫 张探军 刘峰春 叶俊杰 《车用发动机》 北大核心 2024年第4期85-92,共8页
准确、有效的故障诊断是柴油机安全可靠运行的重要保障。基于热工参数诊断的方法存在测点多、专业性强等问题,传统机器学习结合振动信号诊断方法存在人为影响因素过高、不确定性大等问题,因此提出了一种基于1DCNN-GWO-SVM的柴油机喷油... 准确、有效的故障诊断是柴油机安全可靠运行的重要保障。基于热工参数诊断的方法存在测点多、专业性强等问题,传统机器学习结合振动信号诊断方法存在人为影响因素过高、不确定性大等问题,因此提出了一种基于1DCNN-GWO-SVM的柴油机喷油系统故障诊断方法。首先利用一维卷积神经网络(one-dimensional convolutional neural network,1DCNN)对时域下的柴油机振动加速度信号进行自学习特征提取,然后利用提取到的特征向量训练支持向量机(support vector machine,SVM)分类模型,并利用灰狼优化算法(grey wolf optimization,GWO)对SVM的C,g等超参数进行寻优,以此来实现对柴油机的“端对端”故障诊断。在实例验证中,1DCNN-GWO-SVM在测试集上能达到99.10%的诊断准确率,优于传统的机器学习故障诊断方法,并且在信噪比为分别10 dB,20 dB,30 dB的干扰环境下,依然能保持90%以上的诊断准确率。结果表明:1DCNN-GWO-SVM是一种预测精度高、泛化能力强、抗干扰能力强的柴油机“端对端”喷油系统故障诊断方法,具有实际工程应用价值。 展开更多
关键词 卷积神经网络 支持向量机 灰狼优化算法 柴油机 故障诊断
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柴油发动机数字孪生的应用及实现
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作者 宋国梁 付文杰 《内燃机与配件》 2024年第14期115-117,共3页
数字孪生技术已经成为了工业制造中最新的研究热点之一。本文旨在探讨数字孪生技术在柴油发动机故障诊断方面的应用。首先,本文介绍了数字孪生技术的基本概念和原理,包括模型构建、数据采集和仿真分析等方面。然后,本文详细阐述了数字... 数字孪生技术已经成为了工业制造中最新的研究热点之一。本文旨在探讨数字孪生技术在柴油发动机故障诊断方面的应用。首先,本文介绍了数字孪生技术的基本概念和原理,包括模型构建、数据采集和仿真分析等方面。然后,本文详细阐述了数字孪生技术在柴油发动机故障诊断方面的应用,包括基于传感器数据的实时监测、基于复杂模型的故障诊断和预测性维护等方面。最后,本文基于试验数据,通过机器学习算法,实现了对柴油发动机排气温度的预测,可用于基于柴油发动机排气温度的故障诊断。 展开更多
关键词 发动机 数字孪生 机器学习 故障诊断
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应用优化DHKELM的柴油机故障诊断方法
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作者 刘子昌 白永生 +1 位作者 韩月明 贾希胜 《陆军工程大学学报》 2024年第1期77-85,共9页
为准确、高效地对柴油机故障进行诊断,提出应用优化深度混合核极限学习机(deep hybrid kernel extreme learning machine, DHKELM)的柴油机故障诊断方法。该方法以各样本的频谱幅值作为故障特征,归一化处理后作为DHKELM模型的输入,从而... 为准确、高效地对柴油机故障进行诊断,提出应用优化深度混合核极限学习机(deep hybrid kernel extreme learning machine, DHKELM)的柴油机故障诊断方法。该方法以各样本的频谱幅值作为故障特征,归一化处理后作为DHKELM模型的输入,从而实现对柴油机各故障状态的识别。相较极限学习机,该模型具有更深层次的结构,引入了混合核函数以及自动编码器,可以准确区分易混淆的故障类型,提高诊断准确率。针对DHKELM模型中各个超参数难以确定的问题,提出利用改进麻雀搜索算法(improved sparrow search algorithm, ISSA)对模型中各超参数进行寻优,充分发挥模型的故障诊断性能。实验结果表明,在实验室实测数据中,所提方法较传统方法具有较好的故障诊断精度,为柴油机故障诊断提供了一种新的思路。 展开更多
关键词 柴油机 故障诊断 深度混合核极限学习机 改进麻雀搜索算法
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Aero-engine fault diagnosis applying new fast support vector algorithm 被引量:1
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作者 XU Qi-hua GENG Shuai SHI Jun 《航空动力学报》 EI CAS CSCD 北大核心 2012年第7期1604-1612,共9页
A new fast learning algorithm was presented to solve the large-scale support vector machine ( SVM ) training problem of aero-engine fault diagnosis.The relative boundary vectors ( RBVs ) instead of all the original tr... A new fast learning algorithm was presented to solve the large-scale support vector machine ( SVM ) training problem of aero-engine fault diagnosis.The relative boundary vectors ( RBVs ) instead of all the original training samples were used for the training of the binary SVM fault classifiers.This pruning strategy decreased the number of final training sample significantly and can keep classification accuracy almost invariable.Accordingly , the training time was shortened to 1 / 20compared with basic SVM classifier.Meanwhile , owing to the reduction of support vector number , the classification time was also reduced.When sample aliasing existed , the aliasing sample points which were not of the same class were eliminated before the relative boundary vectors were computed.Besides , the samples near the relative boundary vectors were selected for SVM training in order to prevent the loss of some key sample points resulted from aliasing.This can improve classification accuracy effectively.A simulation example to classify 5classes of combination fault of aero-engine gas path components was finished and the total fault classification accuracy reached 96.1%.Simulation results show that this fast learning algorithm is effective , reliable and easy to be implemented for engineering application. 展开更多
关键词 AERO-ENGINE support vector machines fault diagnosis large-scale training set relative boundary vector sample pruning
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Fault diagnosis of diesel engine valve clearance under variable operating condition based on soft interval SVM
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作者 Jiang Zhinong Lai Yuehua +2 位作者 Mao Zhiwei Zhang Jinjie Lai Zehua 《High Technology Letters》 EI CAS 2021年第2期111-120,共10页
The fault detection and diagnosis of diesel engine valve clearance can effectively improve the availability and safety of diesel engine and have extremely important value and significance.Diesel engines generally oper... The fault detection and diagnosis of diesel engine valve clearance can effectively improve the availability and safety of diesel engine and have extremely important value and significance.Diesel engines generally operate in various stable operating conditions,which have important influence on the fault diagnosis.However,many fault diagnosis methods have been put forward under specific stable operating condition based on vibration signal.As the result of great impact caused by operating conditions,corresponding diagnosis models cannot deal with the fault diagnosis under different operating conditions with required accuracy.In this paper,a fault diagnosis of diesel engine valve clearance under variable operating condition based on soft interval support vector machine(SVM)is proposed.Firstly,the fault features with weak condition sensitivity have been extracted according to the influence analysis of fault on vibration signal.Moreover,soft interval constraint has been applied to SVM algorithm to reduce the random influence of vibration signal on fault features.In addition,different machine learning algorithms based on different feature sets are adopted to conduct the fault diagnosis under different operating conditions for comparison.Experimental results show that the proposed method is applicable for fault diagnosis under variable operating condition with good accuracy. 展开更多
关键词 diesel engine fault diagnosis operating condition support vector machine(SVM)
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Fault Prediction with Static Software Metrics in Evolving Software: A Case Study in Apache Ant
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作者 Xue Han Gongjun Yan 《Journal of Computer and Communications》 2022年第2期33-45,共13页
Software testing is an integral part of software development. Not only that testing exists in each software iteration cycle, but it also consumes a considerable amount of resources. While resources such as machinery a... Software testing is an integral part of software development. Not only that testing exists in each software iteration cycle, but it also consumes a considerable amount of resources. While resources such as machinery and manpower are often restricted, it is crucial to decide where and how much effort to put into testing. One way to address this problem is to identify which components of the subject under the test are more error-prone and thus demand more testing efforts. Recent development in machine learning techniques shows promising potential to predict faults in different components of a software system. This work conducts an empirical study to explore the feasibility of using static software metrics to predict software faults. We apply four machine learning techniques to construct fault prediction models from the PROMISE data set and evaluate the effectiveness of using static software metrics to build fault prediction models in four continuous versions of Apache Ant. The empirical results show that the combined software metrics generate the least misclassification errors. The fault prediction results vary significantly among different machine learning techniques and data set. Overall, fault prediction models built with the support vector machine (SVM) have the lowest misclassification errors. 展开更多
关键词 Software engineering fault Prediction Software Metrics machine Learning
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基于KPCA-SVM的S700K转辙机故障诊断方法 被引量:3
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作者 张友鹏 魏智健 +1 位作者 杨妮 张迪 《安全与环境学报》 CAS CSCD 北大核心 2023年第9期3089-3097,共9页
针对S700K转辙机动作功率曲线非线性特征多样化、复杂化的特点,提出了一种基于核主成分分析(Kernel Principal Component Analysis,KPCA)和支持向量机(Support Vector Machine,SVM)的智能故障诊断方法。首先,对S700K转辙机的功率曲线进... 针对S700K转辙机动作功率曲线非线性特征多样化、复杂化的特点,提出了一种基于核主成分分析(Kernel Principal Component Analysis,KPCA)和支持向量机(Support Vector Machine,SVM)的智能故障诊断方法。首先,对S700K转辙机的功率曲线进行分析,研究正常曲线变化规律,总结常见故障类型功率曲线的变化现象和故障原因。然后,从功率曲线中提取10种时域特征值组成初始特征数据集,用KPCA算法将特征数据映射到高维特征空间中对其进行PCA降维,得到故障样本的非线性主成分。最后,将得到的非线性主成分作为多分类SVM的输入样本进行故障模式识别。采用粒子群优化(Particle Swarm Optimization,PSO)算法分别对核函数参数和SVM惩罚因子进行优化,提高模型的诊断精度。仿真结果表明,该模型能够有效提取转辙机故障信号的非线性特征,故障诊断精度达到97%,诊断时间较短,适用于准确性、实时性要求更高的提速道岔。 展开更多
关键词 安全工程 S700K转辙机 故障诊断 核主成分分析 粒子群优化算法 支持向量机
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