[an error occurred while processing this directive] [an error occurred while processing this directive] [an error occurred while processing this directive]
[an error occurred while processing this directive]
CORRELATION TECHNOLOGY

Telemetry Vibration Signal Abnormality Detection Method Based on Multi-scale Spectral Kurtosis Map

  • LIU Xue ,
  • XUAN Zhiwu ,
  • LIANG Hong
Expand
  • No. 91550 Unit, Liaoning Dalian 116000, China

Received date: 2014-11-09

  Online published: 2025-05-28

Abstract

Since telemetry vibration signal in frequency domain has characteristics of complex composition, nonlinear and non-stationary, as well as strong noise, a telemetry vibration signal detection method based on multi-scale spectral kurtosis map was proposed. Firstly, the collected telemetry vibration signal was zero drift amended and eliminated the trend term. Secondly, adaptive decomposition method was used to multi-scale decompose the signal, and the spectral kurtosis was used to remove false component: Thirdly, the time-frequency distribution was calculated by screening component, and divided multi-layer and multi-scale, then the spectral kurtosis of the corresponding scale band signal was calculated to draw spectral kurtosis map, and the filter band was selected based on the principle of maximum kurtosis; Finally, CZT transform was used to refine the filtered signal spectrum for abnormal frequency vibration signal. The results of simulation and practical application demonstrate effectiveness of this method.

Cite this article

LIU Xue , XUAN Zhiwu , LIANG Hong . Telemetry Vibration Signal Abnormality Detection Method Based on Multi-scale Spectral Kurtosis Map[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2015 , 35(5) : 187 -190 . DOI: 10.15892/j.cnki.djzdxb.2015.05.043

[an error occurred while processing this directive]

References

[1]
Nikolaou N G, Antoniadis I A. Demodulation of vibration signals generated by defects in rolling element bearings using complex shifted morel wavelets[J]. Mechanical Systems and Signal Processing, 2002. 16 4: 677-694.
[2]
Antoni J, Randall R B. The spectral kurtosis: a useful tool for characterizing non-stationary signals[J]. Mechanical System s and Signal Processing, 2006. 20 2: 282-307.
[3]
蔡艳平, 李艾华, 石林锁, 等. 基于EMD与谱峭度的滚动轴承故障检测改进包络谱分析[J]. 振动与冲击, 2011. 30 2: 167-472.
[4]
程军圣, 张亢, 杨宇, 等. 局部均值分解方法与经验模式分解的对比研究[J]. 振动与冲击, 2009. 28 5: 13-16.
[5]
杨德昌, 唐巍, 屈瑞谦, 等. 基于改进局部均值分解的低频振荡参数提取[J]. 中国电机工程学报, 2013, 33 4: 135-140.
[6]
Smith J S. The local mean decomposition and its application to EEG perception data[J]. Journal of the Royal Society Interface, 2005, 2 5: 443-454.
[7]
王晓冬, 何正嘉, 訾艳阳. 滚动轴承故障诊断的多小波谱峭度方法[J]. 西安交通大学学报, 2010, 44 3: 77-81.
Outlines

/

[an error occurred while processing this directive]