A signal structure priori enhanced deep compressed sensing method for vibration signals and an improved reconstruction evaluation method in the PHM framework

Authors:
Publisher website
Google Scholar
Abstract
The compressed sensing (CS) methods have two issues when processing vibration signals of rotating machinery: (1) the data-driven method and signal priors (e.g., global/local and structure priors) are not combined well, resulting in the reconstruction not converging to high-precision results; (2) the current metrics only focus on numerical accuracy and do not consider PHM scene fitness. We propose a deep compressed sensing framework (RMVS-DCS): (1) Global-Local Feature Reconstruction (GLFR) blocks alternately reconstruct features at global and local scales. Pre-train the output layer by dictionary learning, introducing the signal structure prior to guiding the model to converge to a better one. (2) A dual evaluation system, combining the numerical indicators and prognostics and health management (PHM) task accuracy, helps the model selection in PHM scenarios. On the rotor unbalance data set, its reconstruction and prediction error are reduced by 82.60% and 89.90% (compression ratio is 0.5), demonstrating the advanced reconstruction precision and PHM compatibility of the framework.

This publication has 46 references indexed in Scilit: