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202404016.pdf
文章编号： 1672-6987（2024）04-0114-07； DOI： 10.16351/j.1672-6987.2024.04.016
吴鑫坤1， 王师1， 刘尚旗2， 刘慧明1*（1.青岛科技大学 自动化与电子工程学院，山东 青岛 266061； 2.青岛港国际有限公司前港分公司， 山东 青岛 266000）
摘要： 针对滚动轴承故障信号包含大量噪声信号和迭代滤波算法存在模态混叠等问题，提出一种自适应局部迭代滤波算法与奇异值分解算法相结合的滚动轴承故障诊断新方法。首先采用自适应局部迭代滤波算法对故障信号进行处理得到若干个内禀模态函数，计算出样本熵后设定阈值进行信号重构；然后进行奇异值分解，绘制差分谱曲线；最后根据差分谱中的突变位置进行二次重构，进一步完成降噪。本工作将该方法应用于凯斯西储大学的轴承数据进行验证，实验结果表明该方法解决了迭代滤波算法存在的模态混叠问题以及大量噪声信号冗余问题，体现了该方法在滚动轴承故障诊断中的有效性。
关键词： 自适应迭代滤波； 奇异值分解； 样本熵； 奇异值差分谱
中图分类号： TP 18文献标志码： A
引用格式： 吴鑫坤， 王师， 刘尚旗， 等. 基于ALIF-SVD的滚动轴承故障诊断［J］. 青岛科技大学学报(自然科学版), 2024, 45(4): 114-120.
WU Xinkun, WANG Shi, LIU Shangqi, et al. Fault diagnosis of rolling bearing based on ALIF-SVD［J］. Journal of Qingdao University of Science and Technology（Natural Science Edition), 2024， 45（4）： 114-120.
Fault Diagnosis of Rolling Bearing Based on ALIF-SVD
WU Xinkun1, WANG Shi1, LIU Shangqi2, LIU Huiming1
（1. College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China;
2. Former Port Branch of Qingdao Port International Co.,Ltd., Qingdao 266000, China）
Abstract: Aiming at the problems of rolling bearing fault signal containing a large number of noise signals and modal aliasing in iterative filtering algorithm, a new method of rolling bearing fault diagnosis based on adaptive local iterative filtering algorithm and singular value decomposition algorithm is proposed. Firstly, the adaptive local iterative filtering algorithm is used to process the fault signal to obtain several intrinsic mode functions, and the sample entropy is calculated and the threshold is set for signal reconstruction. Then singular value decomposition was performed to draw the difference spectrum curve; finally, the secondary reconstruction is carried out according to the mutation position in the difference spectrum to further complete the noise reduction. In this paper, this method is applied to the bearing data of Case Western Reserve University for verification. The experimental results show that this method solves the modal aliasing problem existing in the iterative filtering algorithm and the redundancy problem of a large number of noise signals, which reflects the effectiveness of this method in the fault diagnosis of rolling bearings.
Key words: ALIF; SVD; sample entropy; singular value difference spectrum
收稿日期： 2023-09-15
基金项目： 青岛科技大学科研启动基金项目（010022586）.
作者简介： 吴鑫坤(1998—),女,硕士研究生.*通信联系人.
