Missing Data Imputation Using SVD-KDR Algorithm in Industrial Monitoring Data

doi:10.3901/JME.2021.02.030

Abstract

Abstract: Integrity and availability of data is the basis for information extraction and knowledge discovery in the era of industrial big data. However, data missing frequently occurs in industrial scenarios due to various reasons, such as collection interruption, transmission interference and improper storage, which seriously affects the data value density. In view of this, an interpolation algorithm with high precision and high robustness based on the SVD-KDR algorithm is proposed. The one-dimensional industrial data is converted into a high-dimensional matrix, which makes up for the limitation of the traditional method of interpolating industrial monitoring data directly from the low-dimensional space. By exploring the low-rank characteristics of non-missing data in the interpolation process, a more robust SVD-KDR algorithm model is established with the help of singular value decomposition theory, which effectively reduces the adverse effect of missing data on parameter estimation accuracy. Experimental results show that the proposed algorithm has higher accuracy and robustness at high missing rates compared with the traditional interpolation algorithm. In addition, the proposed method can not only effectively recover the waveform of missing data, but also restore the fluctuating information behind the original data. The SVD-KDR algorithm can effectively solve the problem of missing data, and provides data recovery and information processing tools for industrial big data analysis.

Key words: missing data imputation, phase space reconstruction, singular value decomposition, low-rank approximation

CLC Number:

TH17

CHEN Shuai, ZHAO Ming, GUO Dong, LIN Jing. Missing Data Imputation Using SVD-KDR Algorithm in Industrial Monitoring Data[J]. Journal of Mechanical Engineering, 2021, 57(2): 30-38.

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