基于小波包-Haar小波变换的漏磁检测信号降噪数据压缩方法
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摘要
漏磁检测是对输油管道、油罐等设备缺陷进行无损检测的一种重要方法,但在检测过程中产生的信号数据量庞大,同时会产生多种干扰噪声,出现信号失真、漂移和信号被湮没等问题,对信号进行降噪滤波是缺陷漏磁检测的关键环节。探讨了信号降噪压缩方法,研究了基于小波包-Haar小波算法的漏磁检测信号降噪压缩算法,通过该算法,在对信号数据进行降噪压缩处理同时,保留了高频部分信号特征,从而在降噪后数据解压缩过程中避免了信号失真畸变的现象。
For pipelines,tanks and other equipment defects,magnetic flux leakage inspection is an important non-destructive testing method,but the huge amount of signal data will be generated in the testing process,and it will produce a variety of interference and noise,occurring problems such as signal distortion,drift,and annihilation etc. Signal noise reduction filter is key for magnetic flux leakage defect detection. The signal noise reduction compression method was explored. The compressed MFL signal denoising wavelet packet-Haar wavelet algorithm was studied. By this algorithm,signal data were compressed to reduce noise while the high frequency signal characteristics were retained,resulting in avoiding the phenomenon of signal distortion aberration in data decompressing process.
For pipelines,tanks and other equipment defects,magnetic flux leakage inspection is an important non-destructive testing method,but the huge amount of signal data will be generated in the testing process,and it will produce a variety of interference and noise,occurring problems such as signal distortion,drift,and annihilation etc. Signal noise reduction filter is key for magnetic flux leakage defect detection. The signal noise reduction compression method was explored. The compressed MFL signal denoising wavelet packet-Haar wavelet algorithm was studied. By this algorithm,signal data were compressed to reduce noise while the high frequency signal characteristics were retained,resulting in avoiding the phenomenon of signal distortion aberration in data decompressing process.
引文
[1]邵婷婷,白宗文,周美丽.基于离散小波变换的信号分解与重构[J].计算机技术与发展,2014,24(11):159-161.SHAO T T,BAI Z W,ZHOU M L.Decomposition and Reconstruction of Signal Based on DWT[J].Computer Technology and Development,2014,24(11):159-161.
[2]许允之,方永丽,褚鑫,等.小波包频带-能量重构的异步电动机转子断条故障诊断[J].煤矿机电,2015(2):75-79,82.XU Y Z,FANG Y L,CHU X,et al.Fault Diagnosis of Asynchronous Motor Rotor Broken-Bar of Wavelet Packets Frequency Band-Energy Reconstruction[J].Colliery Mechanical&Electrical Technology,2015(2):75-79,82.
[3]杨杉,王建.基于高阶阈值函数与小波包的混沌信号降噪[J].计算机应用,2014(4):977-979.YANG S,WANG J.Noise Reduction of Chaotic Signals Based on High-order Threshold Function and Wavelet Packet[J].Journal of Computer Applications,2014(4):977-979.
[4]宋志强,孙衍志,张清民,等.基于小波包-阈值的输油管道缺陷漏磁信号降噪研究[J].中国石油和化工标准与质量,2012(11):84-85.
[5]李萍.基于Haar小波和融合HMM的步态识别方法[J].计算机应用与软件,2013,3(3):244-246,254.LI P.Gait Recogntion Method Based on Haar Wavelet and Fused HMMs[J].Computer Applications and Software,2013,3(3):244-246,254.
[6]曾方,唐斌,梁颖,等.导向钻井下传信号Haar提升小波去噪研究[J].计算机测量与控制,2014,22(6):1967-1969.ZENG F,TANG B,LIANG Y,et al.Research of Haar Lifting Wavelet Signal Denoising on Downward Communication of Steering Drilling Tool by Drilling Fluid Displacement Changes[J].Computer Measurement&Control,2014,22(6):1967-1969.
[7]李宏坤,刘洪轶,徐福健,等.连续小波最优重构尺度确定方法与故障早期识别[J].机械工程学报,2014,50(17):69-76.LI H K,LIU H Y,XU F J,et al.Method for the Optimal Continuous Wavelet Reconstruction Scale Determination and Early Fault Classification[J].Journal of Mechanical Engineering,2014,50(17):69-76.
[8]唐贵基,邓飞跃,何玉灵,等.基于时间-小波能量谱熵的滚动轴承故障诊断研究[J].振动与冲击,2014,33(7):68-72,91.TANG G J,DENG F Y,HE Y L,et al.Rolling Element Bearing Fault Diagnosis Based on Time-wavelet Energy Spectrum Entropy[J].Journal of Vibration and Shock,2014,33(7):68-72,91.
[2]许允之,方永丽,褚鑫,等.小波包频带-能量重构的异步电动机转子断条故障诊断[J].煤矿机电,2015(2):75-79,82.XU Y Z,FANG Y L,CHU X,et al.Fault Diagnosis of Asynchronous Motor Rotor Broken-Bar of Wavelet Packets Frequency Band-Energy Reconstruction[J].Colliery Mechanical&Electrical Technology,2015(2):75-79,82.
[3]杨杉,王建.基于高阶阈值函数与小波包的混沌信号降噪[J].计算机应用,2014(4):977-979.YANG S,WANG J.Noise Reduction of Chaotic Signals Based on High-order Threshold Function and Wavelet Packet[J].Journal of Computer Applications,2014(4):977-979.
[4]宋志强,孙衍志,张清民,等.基于小波包-阈值的输油管道缺陷漏磁信号降噪研究[J].中国石油和化工标准与质量,2012(11):84-85.
[5]李萍.基于Haar小波和融合HMM的步态识别方法[J].计算机应用与软件,2013,3(3):244-246,254.LI P.Gait Recogntion Method Based on Haar Wavelet and Fused HMMs[J].Computer Applications and Software,2013,3(3):244-246,254.
[6]曾方,唐斌,梁颖,等.导向钻井下传信号Haar提升小波去噪研究[J].计算机测量与控制,2014,22(6):1967-1969.ZENG F,TANG B,LIANG Y,et al.Research of Haar Lifting Wavelet Signal Denoising on Downward Communication of Steering Drilling Tool by Drilling Fluid Displacement Changes[J].Computer Measurement&Control,2014,22(6):1967-1969.
[7]李宏坤,刘洪轶,徐福健,等.连续小波最优重构尺度确定方法与故障早期识别[J].机械工程学报,2014,50(17):69-76.LI H K,LIU H Y,XU F J,et al.Method for the Optimal Continuous Wavelet Reconstruction Scale Determination and Early Fault Classification[J].Journal of Mechanical Engineering,2014,50(17):69-76.
[8]唐贵基,邓飞跃,何玉灵,等.基于时间-小波能量谱熵的滚动轴承故障诊断研究[J].振动与冲击,2014,33(7):68-72,91.TANG G J,DENG F Y,HE Y L,et al.Rolling Element Bearing Fault Diagnosis Based on Time-wavelet Energy Spectrum Entropy[J].Journal of Vibration and Shock,2014,33(7):68-72,91.