基于自适应随机共振的水轮机组故障诊断研究  

Research on Fault Diagnosis of Hydraulic Turbine Based on Adaptive Stochastic Resonance

作　　者：王谊 Wang Yi(School of Aeronautical Engineering,Shaanxi Polytechnic Institute,Xianyang Shaanxi 712000,China)

机构地区：[1]陕西工业职业技术学院航空工程学院,陕西咸阳712000

出　　处：《陕西工业职业技术学院学报》2021年第1期1-6,9,共7页Journal of Shaanxi Polytechnic Institute

基　　金：基于多稳随机共振的机电设备故障检测方法研究(项目编号:2020TKYB-043)。

摘　　要：针对水轮机组早期故障信号检测提出了自适应三稳态随机共振检测方法。分析了三穗态随机共振系统的起振条件,研究α噪声背景下基于三稳随机共振的微弱信号检测方法。并针对实际工程中微弱信号的实时性检测问题,提出以输出信噪比作为适应度函数,采用粒子群算法对三穂态随机共振的结构参数a,b,c进行同步优化。以多频微弱周期信号为待测信号进行数值仿真,并将该方法用于水轮机组故陣信号诊断中,实验结果均表明,该方法能快速有效地检测出淹没在强嗓声背景下的早期故障信号,为其工程应用奠定了理论基础。This research proposed a tristable stochastic resonance fault diagnosis method for the early fault signal detection of hydraulic turbine units,analyzed the resonance conditions of the system,and reviewed the weak signal detection method based on the tristable stochastic resonance under the background of a noise.Then,for the realtime detection of weak signals in practical engineering,the tristable stochastic resonance system parameters a,b,c were optimized by particle swarm optimization(PSO),which took the output signal-to-noise ratio(SNRout)as the fitness function.Finally,multi-frequency weak signals detection with a stable noise was achieved,and the method above was applied to the vibration fault of diagnosis turbine.Both simulation and experiment results showed that this method can quickly and effectively detect early default signals submerged in strong noise background,which lays a theoretical foundation for the application in engineering practice.

关 键 词：随机共振 水轮机组 微弱信号检测 粒子群算法 适应度函数 起振条件 故障信号检测 同步优化 

分 类 号：TH165.3[机械工程—机械制造及自动化]