强背景噪声下的小波熵模极大值消噪方法

针对矿井提升机特征信号在强非平稳和强噪声背景下难以有效提取的问题,结合小波能量熵理论与小波模极大值的奇异性理论,提出一种有效的强背景噪声下提升机信号消噪方法。该方法利用信息熵能定量描述时-频域能量概率分布的特性,采用小波熵自适应确定噪声阈值,有效去除随小波分解尺度增大而不断减小的小波模极大值,保留随尺度增大而增大的模极大值,并重构经有效过滤的剩余小波模极大值,实现强背景噪声下噪声信号与真实信号的有效分离。通过对仿真信号和提升机实测信号的应用,表明了该方法消噪效果明显,消噪数据可靠,提高了强背景噪声下提升机故障诊断的数据可靠性。

Wavelet Maximum Modulus De-noising of Energy Entropy in Strong Background Noise

It is difficult to effectively extract the eigen information for mine hoister in the background of strong non-stationary vibration signals and strong noise.An effective extracting method of mine hoister fault information was proposed by combining with the wavelet maximum modulus singularity theory and wavelet energy entropy theory in the background of strong noise.According to the character that information entropy could quantitatively describe energy probability distribution in time-frequency domain,the method of adaptively fixing the noise threshold based on wavelet entropy could effectively remove the continuously decreasing wavelet maximum modulus with the wavelet decomposition scale increasing,which could reserve the continuously increasing wavelet maximum modulus with the wavelet decomposition scale increasing,and reconstructed the remaining wavelet maximum modulus after the effectively filtering.The method effectively separated the real signal from strong background noise.The simulation signal and the measured mine hoister data showed that this method could acquire good de-noising effect and reliable data,and improve the data reliability of mine hoister fault diagnosis in strong noise background.