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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

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).

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%.
- Big data,
- Data-processing,
- Fourier transform,
- Modeling,
- Chemometrics
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