A New Method for Long-Distance Data Reconstruction in Big Data Environment

Abstract

In a big data environment, the data from long-term monitoring are of great importance in intelligent diagnoses, prognostic and health management of industrial components. However, in practice, it is difficult to ensure high reconstructive accuracy making the preservation performance of low-frequency information (e.g., periodic fault impulses) stable. To address this issue, this article proposes a new data reconstruction method that combines a smoothing sparse low-rank matrix (SSLRM) with online dictionary learning. This article first presents the design of SSLRM model for decomposing the raw data into a low resonance component (LRC) and a high resonance component (HRC), such that the periodic fault impulse can be isolated and preserved in advance. Then, both LRC and HRC are, respectively, reconstructed using an online dictionary learning algorithm, i.e., Hankel K-singular value decomposition; thus, the reconstructed data can be obtained accordingly. Both simulation case and engineering case are applied to show the effectiveness and practicality of the proposed approach.