Dimension Reduction of Multi-Source Time Series Sensor Data for Industrial Process
Jiao Meng, Xin Huo, Changchun He, Chao Zhu · 2024
With the advent of big data era, industrial process data has become increasingly large, which are characterized by large-scale, multi-source, and it’s difficult to analyze these high-dimensional data directly. In order to solve this problem, dimension reduction of high-dimensional data is necessary, which saves a lot of resources for subsequent data processing. This paper proposes a dimension reduction method to reduce scale for time series and analyze the correlation between multi-source sensors. For time series with large time scales, an adaptive largesttriangle-three-buckets method is proposed, which adaptively selects the optimal bucket number according to the similarity between downsampled data and original data of multi-source data. Further, robust principal component analysis is used to decompose the high-dimensional data into low-rank matrix and sparse matrix. The low-rank matrix represents the low-dimension principal component of multi-source data, and correlation analysis reduces its dimension further. Experiments are carried out on industrial excavator dataset to verify the effectiveness and preponderance of the method.