基于CPO-ICEEMDAN-WTD的称重信号去噪方法研究OA
Research on weighing signal denoising based on CPO-ICEEMDAN-WTD
车辆轴重信号去噪对提高动态称重精度有重要的作用.针对噪声干扰问题,文中提出一种基于冠豪猪优化(CPO)算法优化改进自适应噪声完备经验模态分解(ICEEMDAN)、样本熵(SampEn)以及小波软阈值去噪(WTD)的混合信号去噪方法.首先,利用CPO优化ICEEMDAN的白噪声幅值权重和噪声添加次数,并对车辆的轴重信号进行ICEEMDAN分解,得到若干本征模态分量;然后,计算各分量的样本熵,利用阈值判断含噪分量和有用分量,并对含噪分量进行小波软阈值去噪;最后,将处理后的分量与有用分量重构,得到去噪信号.实验结果表明,所提方法可以有效去除原始轴重信号中的噪声,进而提高动态称重系统的测量精度.
Denoising vehicle axle weight signals play an important role in improving dynamic weighing accuracy.In allusion to the problem of noise interference,a method of hybrid signal denoising based on the crowned porcupine optimization(CPO)algorithm to optimize and improve adaptive noise complete empirical mode decomposition(ICEEMDAN),sample entropy(SampEn),and wavelet soft threshold denoising(WTD)is proposed.The CPO is used to optimize the white noise amplitude weight and the number of noise additions of ICEEMDAN,and the axle load signal of vehicle is decomposed by ICEEMDAN to obtain several intrinsic modal components.The SampEn of each component is calculated,and the threshold is used to distinguish noisy components from useful components,and perform WTD on the noisy components.The denoised signal is constructed by combining the processed components with the useful components.The experimental results show that the proposed method can effectively remove the noise in the original axle weight signal,thereby improving the measurement accuracy of the weight-in-motion system.
赵栓峰;闵雨轩;李小雨
西安科技大学 机械工程学院,陕西 西安 710054西安科技大学 机械工程学院,陕西 西安 710054西安科技大学 机械工程学院,陕西 西安 710054
信息技术与安全科学
动态称重信号滤波经验模态分解小波软阈值去噪冠豪猪优化算法信号分解和重构样本熵
weight-in-motionsignal filteringempirical mode decompositionwavelet soft threshold denoisingcrowned porcupine optimization algorithmsignal decomposition and reconstructionsample entropy
《现代电子技术》 2026 (6)
145-151,7
陕西省秦创原"科学家+工程师"队伍建设项目(24KGDW0041)
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