Efficient compressive sensing for machinery vibration signals

TL;DR

This paper evaluates compressive sensing techniques for machinery vibration signals, introducing a hardware-efficient measurement matrix that improves reconstruction quality at high compression ratios.

Contribution

It provides a comprehensive comparison of CS components and introduces the Wang matrix for vibration signal acquisition, demonstrating superior performance.

Findings

Wang matrix outperforms Gaussian and Bernoulli matrices in reconstruction quality.

The proposed CS framework achieves high SNR at high compression ratios.

Experimental results validate the effectiveness of the new measurement matrix.

Abstract

Mechanical vibration monitoring often requires high sampling rates and generates large data volumes, posing challenges for storage, transmission, and power efficiency. Compressive Sensing (CS) offers a promising approach to overcome these constraints by exploiting signal sparsity to enable sub-Nyquist acquisition and efficient reconstruction. This study presents a comprehensive comparative analysis of the key components of the CS framework: sparse basis, measurement matrix, and reconstruction algorithm for machinery vibration signals. In addition, a hardware-efficient measurement matrix, the Wang matrix, originally developed for image compression, is introduced and evaluated for the first time in this context. Experimental assessment using the HUMS2023 and the CETIM gearbox datasets demonstrates that this matrix achieves superior reconstruction quality, with higher SNR, compared to conventional Gaussian and Bernoulli matrices, especially at high compression ratios.