Noise reduction method for wire rope damage signal under strong noise background
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摘要: 钢丝绳损伤信号是一种非平稳无周期性的冲击信号，其特征信号的降噪处理和特征提取成为亟待解决的难题。小波变换方法若小波基或者分解层数不适合，会在信号降噪的同时引入其他噪声干扰，影响信号处理与特征提取的效果。相较于小波变换方法，移位平均法只需要选择一定的移位窗宽即可实现对信号的有效降噪，但移位窗宽需要人为选择，盲目性大。针对上述问题，提出一种强噪声背景下钢丝绳损伤信号降噪方法。利用钢丝绳漏磁检测传感器采集不同类型的断丝数据，向信号中加入强高斯白噪声，以模拟强噪声背景；采用自适应移位平均法对钢丝绳损伤信号进行降噪，利用量子粒子群优化(QPSO)算法优化移位平均法的窗宽；将损伤信号的信噪比(SNR)作为适应度函数，通过QPSO算法使得损伤特征信号SNR最大化，从而实现最优信号降噪效果。实验结果表明，对于强噪声背景下的钢丝绳平稳和波动信号，相较于小波变换，自适应移位平均法的降噪效果更明显，信噪比更高，信号更为平滑。实测结果表明，对于现场采集的噪声相对弱一些的钢丝绳损伤信号，自适应移位平均法的降噪效果也比小波变换好，验证了自适应移位平均法具有较好的通用性。Abstract: The wire rope damage signal is a kind of non-stationary and non-periodic impact signal, and the noise reduction processing and characteristic extraction of its characteristic signal become difficult problems to be solved urgently. If the wavelet base or decomposition layer number of wavelet transform method is not suitable, which will introduce other noise interference while reducing signal noise, and affect the effect of signal processing and characteristic extraction. Compared with the wavelet transform, the moving average method only needs to select a certain shift window width to achieve effective noise reduction, but the shift window width needs to be selected artificially, and the blindness is large. In order to solve the above problems, a noise reduction method of wire rope damage signal under strong noise background is proposed. Different types of broken wire data are collected by magnetic flux leakage (MFL) sensor of wire rope, and strong Gaussian white noise is added to the signal to simulate the strong noise background. The adaptive moving average method is used to reduce the noise of the wire rope damage signal, and the quantum particle swarm optimization (QPSO) algorithm is used to optimize the window width of the moving average method. The signal-to-noise ratio (SNR) of the damage signal is used as the fitness function, and the SNR of damage characteristic signal is maximized by the QPSO algorithm, so as to achieve the optimal signal noise reduction effect. The experimental results show that compared with wavelet transform, the adaptive moving average method has more obvious noise reduction effect, higher signal-to-noise ratio and smoother signal for wire rope stationary and fluctuating signals under strong noise background. The measured results show that the noise reduction effect of the adaptive moving average method is also better than that of the wavelet transform for the wire rope damage signals with relatively weak noise on site, which verifies that the adaptive moving average method has good universality.
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图 3 处理平稳信号的QPSO算法迭代曲线
Figure 3. Iterative curve of QPSO Algorithm for stationary signal processing
图 6 处理波动信号的QPSO算法迭代曲线
Figure 6. Iterative curve of QPSO Algorithm for fluctuating signal processing
图 9 处理实测信号的QPSO算法迭代曲线
Figure 9. Iterative curve of QPSO Algorithm for measured signal processing
表 1 钢丝绳损伤信号处理结果对比
Table 1. Comparison of processing results for wire rope damage signal
处理方法 SNR/dB 损伤
信号1损伤
信号2损伤
信号3自适应
移位平均法−0.885 43 0.017 1 3.633 14×10-7 基于sym4的
小波变换−0.885 48 −0.777 9 3.632 60×10-7 基于db2的
小波变换−0.885 51 −0.780 6 3.633 06×10-7 基于db5的
小波变换−0.885 53 −0.780 1 3.632 70×10-7 
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