基于经验模态分解和相似性挖掘的水电机组振动故障诊断研究

项目名称： 基于经验模态分解和相似性挖掘的水电机组振动故障诊断研究

项目编号： No.51209172

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 水利科学与海洋工程学科

项目作者： 李辉

作者单位： 西安理工大学

项目金额： 25万元

中文摘要： 在总结现有研究成果的基础上，采取理论分析和数值仿真相结合的总体技术路线，理论分析与实际情况相互参照，仿真计算与真机观测密切结合，深入研究水电机组振动故障的智能诊断问题。采集原型水轮机振动故障信号数据，对振动信号进行分析处理，获取水轮机振动的故障样本，建立水电机组振动故障的故障集。借鉴现代信号处理与智能化技术领域的最新研究成果，将经验模态分解方法与模糊数学、海量数据挖掘技术相结合，对水电机组振动故障诊断进行应用研究，建立水电机组振动故障的诊断模型。

中文关键词： 水电机组；故障诊断；振动；Hilbert-Huang变换；分形

英文摘要： Based on existing research findings, the intelligent diagnosis problems of vibration fault for hydropower units were investigated in-depth by integrated various methods such as theoretical analysis, numerical simulation and experimental investigation. Through collecting the vibration signal data of the water power station with the fault diagnosis system and simulating the partial vibration fault in a model hydraulic turbine test rig, the fault samples of vibration fault were obtained, then these vibration signal data were analyzed and processed to set up the fault sets of hydropower units vibration fault. By reference from latest research achievements of modern signal processing and artificial intelligence fields, and by using lifting algorithm-based 2nd generation wavelet transform, wavelet packet decomposition, rough set reduction and support vector machines, and so on, the vibration signal de-noising, fault features extracting and fault diagnosis methods of hydropower units vibration fault were carried out to propose a intelligent on-line diagnosis method for the hydropower units vibration fault and to establish a diagnosis model of hydraulic turbine vibration fault.

英文关键词： hydropower unit；fault diagnosis；vibration；Hilbert- Huang transform；fractal