一种基于流形学习和KNN算法的柴油机工况识别方法  

Operating Mode Identification Method for Diesel Engines based on Manifold Learning and KNN Algorithm

作　　者：江志农 赵南洋 夏敏 赵飞松 高佳丽 张进杰[3] JIANG Zhinong;ZHAO Nanyang;XIA Min;ZHAO Feisong;GAO Jiali;ZHANG Jinjie(Beijing Key Laboratory of Health Monitoring Control and Fault Self-recovery for High-endMachinery,Beijing University of Chemical Technology,Beijing,100029,China;Sinopec Chongqing Natural Gas Pipeline Co.,Ltd.,Chongqing 408000,China;Compressor Health and Intelligent Monitoring Center of National Key Laboratory of CompressorTechnology,Beijing University of Chemical Technology,Beijing 100029,China)

机构地区：[1]北京化工大学高端机械装备健康监控与自愈化北京市重点实验室,北京100029 [2]中石化重庆天然气管道有限责任公司,重庆408000 [3]北京化工大学压缩机技术国家重点实验室压缩机健康智能监控中心,北京100029

出　　处：《噪声与振动控制》2019年第3期1-6,共6页Noise and Vibration Control

基　　金：国家“863”计划资助项目(2014AA041806);国家重点研发计划资助项目(2016YFF0203305);中央高校基本科研业务费专项资金资助项目(JD1815);双一流建设专项经费资助项目(ZD1601)

摘　　要：不同负荷状态下的柴油机振动、温度、转速等信号显著不同,而机组故障信号特征往往被淹没在随负荷变化而剧烈变化的信号中,因此变负荷状态下的柴油机故障监测诊断难度较大,一直困扰着柴油机的实际故障诊断工作。提出一种基于流形学习和KNN算法的柴油机工况识别方法,为柴油机变负荷工况下故障监测预警打下基础。方法融合机组的多源信号特征构建特征向量,通过流形学习t-分布邻域嵌入算法(t-SNE)实现特征向量的维数约简和敏感特征提取,采用K最近邻分类算法(KNN)完成柴油机运行负荷状态的自动分类。正常及故障状态下多组柴油机监测数据的处理结果验证了方法的有效性和实用性。Under different load conditions,the signals of vibration,temperature,speed,etc.,of diesel engines are significantly different,and the fault signal characteristics of the engine unit are often submerged in signals that change drastically with the load change.Therefore,the diesel engine’s fault monitoring and diagnosing under variable load conditions is difficult and the drastically changed signals always troubles the actual fault diagnosis.This paper presents an operating mode identification method for diesel engines based on manifold learning and KNN algorithm,which lays a foundation for fault monitoring and early warning of diesel engines under variable load conditions.The method combines the multi-source signal features of the unit to construct the feature vector.The feature reduction and sensitive feature extraction of the feature vector is achieved through the manifold learning t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm.The K-Nearest Neighbor (KNN) classification algorithm is used to complete the automatic classification of diesel engine’s operating load status.The diesel engine signal under normal and fault conditions verifies the effectiveness and practicality of this method.

关 键 词：振动与波 柴油机 变负荷 流形学习 KNN 敏感特征 

分 类 号：TK42[动力工程及工程热物理—动力机械及工程] TH165.3[机械工程—机械制造及自动化]