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基于 SVD-HT 和 CSF-PPSO-ESN 方法的轴不对中故障的速度信号定量诊断

Shafting Misalignment Malfunction Quantitative Diagnosis Based on Speed Signal SVD-HT and CSF-PPSO-ESN Method.

机构信息

Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), 37 Miaoling Road, Qingdao 266001, China.

Qingdao Industrial and Trade Vocational School, Qingdao 266041, China.

出版信息

Comput Intell Neurosci. 2022 Aug 30;2022:7016597. doi: 10.1155/2022/7016597. eCollection 2022.

DOI:10.1155/2022/7016597
PMID:36082355
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9448552/
Abstract

Aiming at the quantitative diagnosis of shafting misalignment malfunction, a novel method based on speed signal with singular value decomposition and Hilbert transform (SVD-HT) and cubic spline fitting-Pareto particle swarm optimization-echo state network (CSF-PPSO-ESN) method is proposed. The malfunction diagnosis mechanism based on the speed signal is obtained by constructing the shaft misalignment malfunction model. Then, the SVD-HT and CSF-PPSO-ESN methods are applied to obtain the relationship between the shaft misalignment malfunction and the amplitude of the time and the rotation frequency ( ) component of the speed signal. The parameters of the CSF-PPSO-ESN method are settled according to the shaft misalignment malfunction and the component of the speed signal. The accuracy of the proposed method is verified by using the component of the speed signal and the trained CSF-PPSO-ESN to obtain the value of the shaft misalignment malfunction. The repeating experimental results show that the diagnosing error of the shaft misalignment malfunction can reach less than ±10 m. The method presented in this paper provides a novel way to diagnose shaft misalignment malfunction quantitatively.

摘要

针对轴不对中故障的定量诊断问题，提出了一种基于奇异值分解和希尔伯特变换（SVD-HT）与三次样条拟合-帕累托粒子群优化-回声状态网络（CSF-PPSO-ESN）方法的新方法。通过构建轴不对中故障模型，得到基于转速信号的故障诊断机理。然后，应用 SVD-HT 和 CSF-PPSO-ESN 方法获得轴不对中故障与转速信号的时间和旋转频率（ ）分量的幅值之间的关系。根据轴不对中故障和转速信号的 分量确定 CSF-PPSO-ESN 方法的参数。利用转速信号的 分量和训练好的 CSF-PPSO-ESN 来获取轴不对中故障的值，验证了所提方法的准确性。重复实验结果表明，轴不对中故障的诊断误差可以达到±10m 以内。本文提出的方法为定量诊断轴不对中故障提供了一种新的途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18ee/9448552/0b52ceb9727c/CIN2022-7016597.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18ee/9448552/6884ff8a7f82/CIN2022-7016597.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18ee/9448552/2d3fccfa9d86/CIN2022-7016597.006.jpg
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