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基于差分头脑风暴算法的微弱故障信号检测研究 被引量:2

An adaptive stochastic resonance detection method based on differential brain storm algorithm
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摘要 针对早期故障信号的实时性增强检测问题,研究了基于差分头脑风暴的自适应多稳态随机共振检测方法。首先介绍了多稳随机共振模型及系统结构,分析了系统势阱深度对随机共振检测效应的影响;其次,以平均输出信噪比作为适应度函数,采用差分头脑风暴算法来并行优化系统参数a、b、c,增强系统全局搜索能力,使系统获得最佳的共振输出效应;最后,利用自适应多稳随机共振方法检测多个含噪高频微弱信号,并将其应用于单晶炉的振动故障信号检测中。实验及实践结果表明:该方法能实现强噪声背景中微弱信号的检测,为工程应用中早期故障信号的快速增强检测奠定了基础。 Aiming at the real-time enhanced detection of early fault signals,an adaptive multi-stable stochastic resonance detection method based on differential brain storm is studied.Firstly,the multi-stable stochastic resonance model and system structure are introduced,meanwhile,the influence of the system potential well depth on the stochastic resonance detection effect is analyzed;secondly,the average output signal-to-noise ratio is used as the fitness function in the differential brainstorming algorithm,which is used to optimize the system parameters a,b,c simultaneously and enhance the system’s global search capability.The system can improve the best resonance output effect by above algorithm.Finally,the multiple noisy high-frequency weak signals are detected by using the adaptive multi-stable stochastic resonance method,and which is applied to the vibration fault signal detection of single crystal furnace.Experimental and practical results show that the method can realize detection of weak signals in strong noise background,and lay a foundation for rapid enhanced detection of early fault signals in engineering applications.
作者 王谊 焦尚彬 WANG Yi;JIAO Shangbin(School of Aeronautical Engineering,Polytechnic Institute,Xian Yang,Shangxi 012000,China;School of Automation and Information Engineering,Xi’an University of Technology,Xi'an 710040,China)
出处 《自动化与仪器仪表》 2021年第9期66-70,共5页 Automation & Instrumentation
基金 国家自然科学基金面上项目(No.61871318) 陕西工业职业技术学院2020院级科研计划项目(No.2020TKYB-043)。
关键词 多稳随机共振 差分头脑风暴算法 微弱信号 故障诊断 multi-stable stochastic resonance differential brain storm algorithm weak signal fault diagnosis
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