基于多尺度谱峭度图的遥测振动信号异常检测
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摘要
针对遥测振动信号频域成分复杂、非平稳非线性和强噪声特性,提出一种基于多尺度谱峭度图的遥测振动信号异常检测方法。首先对采集到的信号进行零漂修正和趋势项消除,然后利用自适应分解方法对信号进行自适应多尺度分解,依据谱峭度筛选出异常频率所在频带的分量;接下来利用筛选出的分量做时频分布,对时频分布进行多尺度划分,计算相应尺度频段内的谱峭度值并画出谱峭度图,依据峭度最大原则选取滤波频段;最后利用CZT变换对滤波后的信号进行频谱细化,进而获得振动信号的异常频率。仿真和实测数据验证了该方法的有效性。
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.
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.
引文
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[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):167-172.
[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.
[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):167-172.
[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.