The Application of LCS and Information Entropy as a Novel Fusion Algorithm for Degradation Feature Extraction

Abstract

Feature extraction is significant for the prognostics and health management (PHM) of hydraulic pumps. In this paper, a novel fusion algorithm is proposed based on local characteristic-scale decomposition (LCD), composite spectrum (LCS). and information entropy. To make full use of feature information, the LCS is proposed based on the modification of traditional composite spectral algorithm. LCS high-order power entropy and high-order singular entropy, which are relatively defined in Shannon entropy (SE) and Tsallis entropy (TE), are extracted as initial features. Furthermore, the method of feature fusion is presented to modify the features’ conciseness and to improve the performance. Results of the analysis in the experiment indicate that the proposed method is available, and the fused feature is effective in evaluating the pump degradation process.