摘要
针对发动机受工作环境影响,导致油液光谱数据存在大量冗余信息且具有非线性结构,从而影响发动机故障诊断结果的问题,提出监督核熵成分分析(supervised kernel entropy component analysis,SKECA)的特征提取方法;该方法在核熵成分分析基础上引入监督学习算法,提取油液光谱数据的内在几何特征,使提取后的故障特征中包含判别信息,并利用遗传算法(genetic algorithm,GA)寻找参数来优化特征提取的结果,采用支持向量机对故障特征进行分类。结果表明:采用SKECA进行特征提取能够有效提高发动机故障诊断精度。
Focus on the influence of environment on engine operation,which leads to a large amount of redundant information and nonlinear structure in oil spectral data that affects the engine fault diagnosis results,the feature extraction method of SKECA(supervised kernel entropy component analysis)is proposed.A supervised learning algorithm is adopted on the basis of Kernel Entropy Component Analysis,which extracts the inherent geometric features of oil spectrum data to make the extracted fault features include the discriminative information.GA(genetic algorithm)is used to find parameters to optimize the results of feature extraction,and SVM(support vector machine)is used to classify the fault features.Simulation results show that SKECA can effectively improve the accuracy of engine fault diagnosis.
作者
祝志超
吴定会
岳远昌
Zhu Zhichao;Wu Dinghui;Yue Yuanchang(Engineering Research Center of internet of Things Technology Applications Ministry of Education,Jiangnan University,Wuxi 214122,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2022年第1期45-52,共8页
Journal of System Simulation
基金
国家重点研发计划(2020YFB1711102)
国家自然科学基金(61572237)。
关键词
光谱
故障诊断
特征提取
核熵成分分析
spectrum
fault diagnosis
feature extraction
kernel entropy component analysis
