Research on Dynamic Measurement Method of Flow Rate in Tea Processing.

Zhangfeng Zhao, Gaohong Liu, Yueliang Wang, Jiyu Peng, Xin Qiao, Jiang Zhong

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Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Zhangfeng ZhaoCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China.

Gaohong LiuCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China.

Yueliang WangNingbo Yaojiangyuan Machinery Co., Ltd., Ningbo 315444, China.

Jiyu PengCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China.ORCID 0000-0002-2842-170X

Xin QiaoCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China.

Jiang ZhongCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China.ORCID 0000-0001-9614-6730

Funding

National Key R&D Program of China National Key R&D Program of ChinaZhejiang Agricultural Major Technology Collaborative Promotion Plan 2020XTTGCY02-04

6 · The paper itself

Abstract

Tea flow rate is a key indicator in tea production and processing. Due to the small real-time flow of tea leaves on the production line, the noise caused by the transmission system is greater than or close to the real signal of tea leaves. This issue may affect the dynamic measurement accuracy of tea flow. Therefore, a variational mode decomposition combined with a wavelet threshold (VMD-WT) denoising method is proposed to improve the accuracy of tea flow measurement. The denoising method of the tea flow signal based on VMD-WT is established, and the results are compared with WT, VMD, empirical mode decomposition (EMD), and empirical mode decomposition combined with wavelet threshold (EMD-WT). In addition, the dynamic measurement of different tea flow in tea processing is carried out. The result shows that the main noise of tea flow measurement comes from mechanical vibration. The VMD-WT method can effectively remove the noise in the tea dynamic weighing signal, and the denoising performance is better than WT, VMD, EMD, and EMD-WT methods. The average cumulative measurement accuracy of the tea flow signal based on the VMD-WT algorithm is 0.88%, which is 55% higher than that before denoising. This study provides an effective method for dynamic and accurate measurement of tea flow and offers technical support for digital control of the tea processing.

Indexed as

AlgorithmsSignal Processing, Computer-AssistedNoiseSignal-To-Noise RatioTeaTeadenoisingdynamic measurementtea flowvariational empirical mode decompositionwavelet threshold

Identifiers

PMID35684914

DOI10.3390/s22114294

PMCPMC9185642

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