流体机械

2020, v.48;No.580(10) 7-11+70

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变负荷工况下往复压缩机活塞杆运行轴心轨迹特征提取
Axis Orbit Feature Extraction of Reciprocating Compressor Piston Rod under Variable Load Condition

周超;张进杰;张旭东;孙旭;王瑶;
Zhou Chao;Zhang Jinjie;Zhang Xudong;Sun Xu;Wang Yao;Beijing Key Laboratory of Health Monitoring Control and Fault Self-recovery for High-end Machinery Beijing University of Chemical Technology;State Key Laboratory of Compressor Technology(Anhui Provincial Laboratory of Compressor Technology);

摘要(Abstract):

活塞杆是往复压缩机的核心运动部件之一,一旦发生松动、断裂等故障易导致恶性事故。压缩机运行负荷与故障均会导致活塞杆运行状态的改变,从活塞杆运行数据中提取故障特征需排除压缩机运行负荷的影响。本文分别采用改进的离散点包络与信息熵评估方法,从活塞杆轴心轨迹中提取特征参数,采用流形学习方法进行特征降维后构建负荷敏感特征集,与常规包络方法提取特征进行对比,证明了本文方法提取的活塞杆运行状态特征具有更强的敏感性;进一步应用神经网络构建负荷识别模型,实现了压缩机运行负荷的自动识别,为消减负荷影响,实现变负荷工况下压缩机故障特征提取与诊断奠定了基础。
Piston rod is one of the core moving parts of reciprocating compressor.When the piston rod becomes loose or breaks,it tends to cause malignant accidents.Both compressor operating load and fault will lead to the change of operating state of piston rod.It is necessary to extract fault characteristics from operating data of piston rod to eliminate the influence of compressor operating load.In this paper,the improved method of discrete point envelope and information entropy was used to extract the feature parameters of axis orbit from the piston rod.The manifold learning method was used to construct sensitive feature set of load after feature dimension reduction.Compared with the conventional envelope method,it is proved that the operating state characteristics of piston rod extracted by this paper are more sensitive.Furthermore,the neural network was used to construct the load identification model and the automatic identification of compressor operating load was realized,which lays a foundation for reducing the influence of load and realizing fault feature extraction and diagnosis of the compressor under variable load condition.

关键词(KeyWords): 活塞杆;往复压缩机;离散点包络;信息熵;变负荷工况
piston rod;reciprocating compressor;discrete point envelope;information entropy;variable load condition

Abstract:

Keywords:

基金项目(Foundation): 国家重点研发计划项目(2016YFF0203305);; 中央高校基本科研业务费专项资金资助项目(JD1912);; 压缩机技术国家重点实验室(压缩机技术安徽省实验室)开放基金项目(SKL-YSJ201808)

作者(Authors): 周超;张进杰;张旭东;孙旭;王瑶;
Zhou Chao;Zhang Jinjie;Zhang Xudong;Sun Xu;Wang Yao;Beijing Key Laboratory of Health Monitoring Control and Fault Self-recovery for High-end Machinery Beijing University of Chemical Technology;State Key Laboratory of Compressor Technology(Anhui Provincial Laboratory of Compressor Technology);

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