RESEARCH PAPER
A signal structure priori enhanced deep compressed sensing method for vibration signals and an improved reconstruction evaluation method in the PHM framework
1
The Center for Robotics, School of Control Science and Engineering, Shandong University, China
2
The Engineering Research Center of Intelligent Unmanned System, Ministry of Education, China
3
School of Mechanical, Electrical and Information Engineering, Shandong University, China
Submission date: 2025-08-03
Final revision date: 2025-09-19
Acceptance date: 2025-12-21
Online publication date: 2025-12-26
Publication date: 2025-12-26
Corresponding author
Xincheng Tian
The Center for Robotics, School of Control Science and Engineering, Shandong University, China
The Center for Robotics, School of Control Science and Engineering, Shandong University, China
HIGHLIGHTS
- A deep compressed sensing method with the locality/globality and the structure prior.
- The output layer weights are initialized by the data structure information.
- Subsequent PHM tasks accuracy is considered to better evaluate the PHM tasks fitness.
- The method has superior adaptability in the PHM framework.
KEYWORDS
prognostics and health managementdeep compressed sensingsignal structure priorirotating machinery vibration signalreconstruction evaluation method
TOPICS
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.
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