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基于双相干谱的减压阀故障诊断 被引量:10

Fault Diagnosis of Reducing Valve Based on Bispectra Slices
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摘要 双谱包含了信号的非对称、非线性信息,可以用来描述非线性相位耦合,尤其是二次相位耦合;双相干函数可以定量估计二次相位耦合的耦合程度;正常信号和故障信号的双相干谱呈现了不同的波峰特性;二维小波多级分解能在提取信号频率信息的基础上,有效对二维数组信息进行压缩;最小二乘支持向量机可以在有限样本下得到全局最优,从而避免局部最优问题,且具有降低计算复杂度的优点;实验中由获得的26组实验数据,利用二维小波多级分解对双相干谱进行特征提取,并输入LSSVM对减压阀进行故障诊断,取得了正确率接近90%的良好效果。 Bispectrum Includes nonsymmetric, nonlinear information of singals, it can be used to describe nonlinear phase couplings, especially quadratic phase couplings. Bicoherence function can evaluate the degree of quadratic phase couplings quantitatively, the hlcoherence spectrum of normal and fault singals show different peaks characteristics. 2-D wavelet multi-level decomposition can compress the informa- tion of 2--D arrays while extracting features of frequences in primitive signals effectively. LSSVM can find the global optimization solution with small samples, so avoids local optimization, and it can reduce computation complexity. 26 groups of data obtained from the experiment were used to extract features by 2-D wavelet multi level decomposition from bicoherence spectrum, then the features were input to the LSSVM, and diagnose the valve' s faults, the correct rate is nearly 90%, it is satisfactory.
出处 《计算机测量与控制》 CSCD 北大核心 2011年第10期2413-2416,共4页 Computer Measurement &Control
基金 国家自然科学基金(50975098) 2008福建省重大专项课题(2008HZ0002-1)
关键词 减压阀 双相干谱 二维小波 最小二乘支持向量机 故障诊断 pressure relief valve Bieoherence spectrum 2-D wavelet LSSVM fault diagnosis
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  • 1宫伟力,张艳松,安里千.基于图像分割的煤岩孔隙多尺度分形特征[J].煤炭科学技术,2008,36(6):28-32. 被引量:11
  • 2刘树林,唐友福,钟力,孟庆武.活塞压缩机气阀故障的高阶谱特征研究[J].流体机械,2004,32(9):30-32. 被引量:4
  • 3Li Yah, Zhou Wen-an, Song Jun-de. The service utility model in service management. The Journal of China Universities of Posts and Telecommunications, 2005, 12 (4): 21-25
  • 4Lehmann A, Diessel T, Seibold C, et al. Knowledge-based alarm surveillance for TMN. Proceedings of the 1996 IEEE 15th Annual International Phoenix Conference on Computers and Communications, Mar 27-29 1996, Scottsdale, AZ, USA. Piscataway, NJ, USA: IEEE, 1996:494-500
  • 5Hood C, Ji C. Proactive network-fault detection. IEEE Transactions on Reliability, 1997.46(3): 333-341
  • 6Papavassiliou S, Pace M, Zawadzki A. Implementing enhanced network maintenance for transaction access services: tools and applications. Proceedings of 2000 IEEE International Conference on Communications (ICC'00): Vol 1, Jun 18-22, Proceedings of 2000, New Orleans, LA, USA, Piscataway, N J, USA: IEEE, 2000:211--215
  • 7Thottan M, Ji C. Adaptive thresholding for proactive network problem detection. IEEE Third International Workshop on System Management, Apr 7-11, 1998, Newport, RI, USA. Piscataway, NJ, USA: IEEE, 1998:108-116
  • 8Martin T H, Howard B D, Mark B. Neural network design. Dai Kui, et al, trans. Beijing, China: China Machine Press, 2002
  • 9SHI X CH, HUH Y. Diesel engine fault diagnosis and classification [ C ]. International Conference on Signal Processing Proceedings, 2007,1 (8) : 4128841.
  • 10MCCORMICK A C, NANDI A K. Bispectral and trispectral features for machine condition diagnosis [J]. IEEE Proceedings:Vision, Image and Signal Processing, 1999, 146 ( 5 ) : 229-234.

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