离散傅立叶变换用于非连续工业数据分析

引用本文: 孙学辉, 赵冰, 骆震, 孙培健, 彭斌, 聂聪, 邵学广. 离散傅立叶变换用于非连续工业数据分析[J]. 分析化学, 2020, 48(10): 1422-1427. doi: 10.19756/j.issn.0253-3820.201158 shu

Citation: SUN Xue-Hui, ZHAO Bing, LUO Zhen, SUN Pei-Jian, PENG Bin, NIE Cong, SHAO Xue-Guang. Non-continuous Industrial Data Analysis Using Discrete Fourier Transform[J]. Chinese Journal of Analytical Chemistry, 2020, 48(10): 1422-1427. doi: 10.19756/j.issn.0253-3820.201158 shu

离散傅立叶变换用于非连续工业数据分析

孙学辉 ^1, ,
赵冰 ^2, ,
骆震 ^2, ,
孙培健 ^1, ,
彭斌 ^1, ,
聂聪 ^1, ,
邵学广 ^3,

1.
中国烟草总公司郑州烟草研究院, 郑州 450001;
2.
河南中烟工业有限责任公司, 郑州 450000;
3.
南开大学化学学院, 分析科学研究中心, 天津 300071
基金项目:
本文系国家自然科学基金项目（No.21775076）资助

摘要: 大数据分析是当前的研究热点，但由于大数据在数据结构上的复杂性及数据类型上的多样性，大数据分析方法研究仍是数据分析领域中的挑战性问题。本研究建立了一种用于复杂结构数据预处理和建模的分析方法，并应用于分批次采集、采集密度不同且采集时间不统一的非连续工业生产数据分析。利用傅里叶变换得到不同参数的频谱信息，再利用逆变换按照统一的时间点进行数据重构，既得到了时间上统一的各参数数值，还对数据进行了平滑处理和缺失数据的填补。利用重构数据分别建立了4个产品质量指标与5个产品物理参数和原料性能参数之间的定量模型，4个模型的预测值相对偏差平均值均小于5%。

关键词:

English

Non-continuous Industrial Data Analysis Using Discrete Fourier Transform

1.
Zhengzhou Tobacco Research Institute of China National Tobacco Corporation, Zhengzhou 450001, China;
2.
China Tobacco Henan Industry Co. Ltd., Zhengzhou 450000, China;
3.
Research Center for Analytical Sciences, College of Chemistry, Nankai University, Tianjin 300071, China
Received Date: 26 March 2020
Revised Date: 24 August 2020
Fund Project:
This work was supported by the National Natural Science Foundation of China (No. 21775076).

Abstract: Analysis of big data is hot topic for exploring the contained values, however, the development of the analytical methods are still a challenging task due to the complexity in structure and variety. In this work, a method for pre-processing and modeling of the non-continuous industrial data was developed and applied in the analysis of a dataset for an industrial production during six years. Four quality parameters and five production parameters were included and the data were collected in batches and sampled in different time and frequency. Fourier transform was used to obtain the frequency composition of the parameters, and then reconstructed data for each parameter were calculated by the inverse transform using the same time schedule. Therefore, the data of all the parameters at the same time points could be obtained and the missing values in the raw data could be filled, making the reconstructed data suitable for building the model between the quality and production parameters. Furthermore, the smoothing effect could be observed in the reconstructed data. Four models were built for the four quality parameters, all of which had a reliable prediction with the mean bias less than 5%.

Key words:

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离散傅立叶变换用于非连续工业数据分析

English

Non-continuous Industrial Data Analysis Using Discrete Fourier Transform

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离散傅立叶变换用于非连续工业数据分析

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Non-continuous Industrial Data Analysis Using Discrete Fourier Transform

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