Fault Feature Extraction Method of Ball Screw Based on Singular Value Decomposition, CEEMDAN and 1.5DTES

IF 2.3 3区 工程技术 Q2 ENGINEERING, MECHANICAL
Actuators Pub Date : 2023-11-07 DOI:10.3390/act12110416
Qin Wu, Jun Niu, Xinglian Wang
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引用次数: 0

Abstract

In this article, a method is proposed to effectively extract weak fault features and accurately diagnose faults in ball screws, even in the presence of strong background noise. This method combines singular value decomposition (SVD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the 1.5-dimensional spectrum (1.5D) to process and analyze fault vibration signals. The first step involves decomposing the fault signal using the SVD algorithm. The singular values are then screened, and the part of the screen containing more noise information is extracted to complete the first denoising step. The second step involves decomposing the signal after the initial denoising process using CEEMDAN and removing some of the false components from the intrinsic mode function (IMF) components, based on the kurtosis correlation function index. The signal is then reconstructed to complete the second denoising step. Finally, the denoised signal is analyzed using Teager energy operator demodulation and 1.5D spectral analysis to extract the fault frequency and determine the location of the fault in the ball screw. This method has been compared with other denoising methods, such as wavelet packet decomposition combined with CEEMDAN or SVD combined with variational mode decomposition (VMD), and the results show that under the condition of strong background noise, the proposed method can better extract the fault frequency of ball screw.
基于奇异值分解、CEEMDAN和1.5DTES的滚珠丝杠故障特征提取方法
本文提出了一种在强背景噪声下有效提取滚珠丝杠微弱故障特征并准确诊断故障的方法。该方法结合奇异值分解(SVD)、带自适应噪声的全系综经验模态分解(CEEMDAN)和1.5维谱(1.5D)对故障振动信号进行处理和分析。第一步是使用奇异值分解算法对故障信号进行分解。然后对奇异值进行筛选,提取含有较多噪声信息的部分,完成第一步去噪。第二步是在初始去噪处理后使用CEEMDAN对信号进行分解,并根据峰度相关函数指数从本征模态函数(IMF)分量中去除一些假分量。然后重构信号以完成第二步去噪。最后,利用Teager能量算子解调和1.5D频谱分析对降噪后的信号进行分析,提取故障频率,确定滚珠丝杠故障位置。将该方法与小波包分解与CEEMDAN相结合或SVD与变分模态分解(VMD)相结合的去噪方法进行了比较,结果表明,在强背景噪声条件下,该方法能更好地提取滚珠丝杆的故障频率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Actuators
Actuators Mathematics-Control and Optimization
CiteScore
3.90
自引率
15.40%
发文量
315
审稿时长
11 weeks
期刊介绍: Actuators (ISSN 2076-0825; CODEN: ACTUC3) is an international open access journal on the science and technology of actuators and control systems published quarterly online by MDPI.
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