机械振动信号的高效压缩感知
Efficient compressive sensing for machinery vibration signals
AI总结：
本文研究了机械振动信号的压缩感知方法，比较了稀疏基、测量矩阵和重建算法，引入了适用于图像压缩的Wang矩阵，并通过实验验证其在高压缩比下的优越重建性能。
摘要：
机械振动监测通常需要高采样率并产生大量数据，对存储、传输和能耗构成挑战。压缩感知（CS）通过利用信号稀疏性实现子奈奎斯特采样和高效重建。本文全面比较了CS框架的关键组件：稀疏基、测量矩阵和重建算法，引入了最初用于图像压缩的Wang矩阵，并通过HUMS2023和CETIM齿轮箱数据集实验验证，结果显示该矩阵在高压缩比下具有更高的信噪比和更优的重建质量，优于传统高斯和伯努利矩阵。
英文摘要：
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.
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