| 引用本文: | 卓越,倪何,肖鹏飞,何超.一种热工监测参数的模态双分解降噪方法[J].哈尔滨工业大学学报,2025,57(4):162.DOI:10.11918/202401015 |
| ZHUO Yue,NI He,XIAO Pengfei,HE Chao.A modal double-decomposition noise reduction method for thermal monitoring parameters[J].Journal of Harbin Institute of Technology,2025,57(4):162.DOI:10.11918/202401015 |
|
| 摘要: |
| 针对热工监测参数普遍存在异常值、噪声和不规则扰动的问题,从提高监控系统调节控制的精确性和系统运行管理水平的目的出发,提出了一种基于中值模态分解（MREMD）和变分模态分解（VMD）的热工监测参数降噪方法,旨在尽可能保留原始数据有效信息的基础上,降低监控参数的噪声和扰动。首先,对监控参数进行MREMD分解,得到若干本征模态函数（IMF）。其次,通过引入混沌时间序列分析的排列熵筛选出包含噪声的IMF分量重构为原始数据的噪声部分,然后对噪声部分进行VMD分解,以分解所得本征模态函数的最优包络熵为适应度函数,使用北方苍鹰算法（NGO）对VMD分解参数进行寻优,在寻优范围内得到的最低包络熵本征模态函数即噪声部分所含的有效信息。最后,将此部分与MREMD分解所得包含趋势信息的低频IMF分量和残余分量求和重构,得到降噪后的监测信号。结果表明,通过算例验证,本研究提出的模态双分解降噪方法,与主流的各类型小波阈值降噪方法和移动均值滤波法相比,具有更高的信噪比和更低的信息熵及功率谱熵。 |
| 关键词: 数据降噪 中值模态分解（MREMD） C-C算法 信息熵 变分模态分解（VMD） |
| DOI：10.11918/202401015 |
| 分类号:TK39 |
| 文献标识码:A |
| 基金项目:国家自然科学基金面上项目(51909254); 海军工程大学自主研发基金资助项目(425317T014) |
|
| A modal double-decomposition noise reduction method for thermal monitoring parameters |
|
ZHUO Yue1,NI He1,XIAO Pengfei2,HE Chao3
|
|
(1.College of Power Engineering, Naval University of Engineering, Wuhan 430033, China; 2.No.703 Research Institute of China State Shipbuilding Company, Harbin 150078, China; 3.Anqing CSSC Diesel Engine Co., Ltd., Anqing 246001, Anhui, China）
|
| Abstract: |
| Aiming at the problem of outliers, noise and irregular disturbances prevailing in the monitoring parameters of the thermal system, a noise reduction method for monitoring parameter of the thermal system based on median regression empirical mode decomposition (MREMD) and variational mode decomposition (VMD) is proposed. The purpose is to enhance the accuracy of monitoring system regulation and the level of system operation management, while minimizing noise and disturbances in the monitoring parameters, all while preserving as much of the original data’s effective information as possible. The method firstly performs MREMD of the monitoring parameters to obtain a number of intrinsic mode functions (IMF). Secondly, chaotic time series analysis is applied to filter out the IMF components containing noise using permutation entropy, reconstructing them as the noise portion of the original data. Then the noise part is decomposed by VMD, and the optimal envelope entropy of the IMF obtained by the decomposition is used as the fitness function. The northern goshawk optimization (NGO) algorithm is used to optimize the VMD decomposition parameters, yielding the IMF with the lowest envelope entropy within the optimization range, which contains the effective information of the noise portion. Finally, this part was reconstructed by summing with the low frequency IMF component and residual component obtained by MREMD decomposition which both are contained trend information, to obtain the monitoring signal after noise reduction. The results demonstrate that through case studies, the modal double-decomposition noise reduction method proposed in this paper has highest signal-to-noise ratio and lower information entropy and power spectral entropy compared to mainstream wavelet threshold denoising methods and moving average filtering techniques. |
| Key words: data noise reduction median regression empirica mode decomposition(MREMD) C-C algorithm information entropy variational mode decomposition(VMD) |
