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首页> 外文期刊>Measurement and Control: Journal of the Institute of Measurement and Control >Compressed sensing reconstruction for axial piston pump bearing vibration signals based on adaptive sparse dictionary model
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Compressed sensing reconstruction for axial piston pump bearing vibration signals based on adaptive sparse dictionary model

机译：基于自适应稀疏词典模型的轴向活塞泵轴承振动信号的压缩传感重建

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摘要

Aiming at the mechanical equipment in the fault diagnosis process, the traditional Shannon-Nyquist sampling theorem is used for data collection, which faces main problems of storage, transmission, and processing of mechanical vibration signals. This paper presents a novel method of compressed sensing reconstruction for axial piston pump bearing vibration signals based on the adaptive sparse dictionary model. First, vibration signals were divided into blocks, and an energy sequence was produced in accordance with the energy of each signal block. Second, the energy sequence of each signal block was classified by the quantum particle swarm optimization algorithm. Finally, the reconstruction of machinery vibration signals was carried out using the K-SVD dictionary algorithm. The average relative error of the reconstructed signal obtained by the proposed algorithm is 4.25%, and the reconstruction time decreases by 43.6% when the compression ratio is 1.6.
机译：针对故障诊断过程中的机械设备，传统的Shannon-Nyquist采样定理用于数据收集，这面临了机械振动信号的存储，传输和处理的主要问题。 本文提出了一种基于自适应稀疏词典模型的轴向活塞泵轴承振动信号的压缩传感重建的新方法。 首先，将振动信号分成块，并且根据每个信号块的能量产生能量序列。 其次，通过量子粒子群优化算法对每个信号块的能量序列进行分类。 最后，使用K-SVD字典算法进行机械振动信号的重建。 通过所提出的算法获得的重建信号的平均相对误差为4.25％，当压缩比为1.6时，重建时间减小了43.6％。

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