摘要
液压泵长期处于高压、高速的运行工况下,泵体零部件极易发生故障。实际工况下测量的振动信号往往包含着许多无关信号成分如噪声,导致传统方法难以实现故障类型的准确识别。提出一种基于动模式分解(DMD)和t分布随机近邻嵌入(t-SNE)聚类的液压泵故障模式识别方法。在泵体布置传感器进行监测获得振动信号,首先利用DMD进行分解获得表征信号本质特征的模式分量,再利用t-SNE进行降维聚类,实现不同故障类型的准确识别。通过数值仿真和试验台故障数据分析,验证了提出方法的可行性及有效性。
Hydraulic pump is under long-term high-pressure and high-speed operating conditions,the parts of the pump body are very easy to failure.Vibration signals measured under actual working conditions often contain many irrelevant signal components such as noise,which makes it difficult for traditional methods to accurately classify and identify fault types.Therefore,a hydraulic pump fault pattern recognition method was proposed based on dynamic mode decomposition(DMD)and t-distributed random nearest neighbor embedding(t-SNE)clustering.The vibration signal was obtained by monitoring the sensor arranged on the pump body.DMD was used to decompose the signal to obtain the mode components characterized the essential characteristics of the signal,then t-SNE was used for dimensionality reduction clustering to achieve accurate identification of different fault types.The feasibility and effectiveness of the method proposed are verified by numerical simulation and test bench experiment.
作者
金林彩
叶杰凯
张珍
汤小明
邵锡余
庹帅
JIN Lincai;YE Jiekai;ZHANG Zhen;TANG Xiaoming;SHAO Xiyu;TUO Shuai(Lishui Special Equipment Inspection Institute,Lishui Zhejiang 323000,China;Wuhan University of Science and Technology,Wuhan Hubei 430081,China)
出处
《机床与液压》
北大核心
2021年第14期187-192,200,共7页
Machine Tool & Hydraulics
基金
国家自然科学基金项目(51805382)
浙江省市场监督管理局科研计划项目(20190134)。
关键词
动模式分解
t分布随机近邻嵌入
液压泵
故障分类
Dynamic mode decomposition
t-distributed stochastic neighbor embedding
Hydraulic pump
Fault classification
