Resonance Sparse Decomposition Method for Optimizing Quality Factor of Frog Leaping Algorithm

Zengping Kuang; Qingbin Tong; Jing Du; Xiuyong Zhao

doi:10.16578/j.issn.1004.2539.2020.11.006

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Resonance Sparse Decomposition Method for Optimizing Quality Factor of Frog Leaping Algorithm

Theory·Reserach | 更新时间：2022-11-10

- Resonance Sparse Decomposition Method for Optimizing Quality Factor of Frog Leaping Algorithm
- Journal of Mechanical Transmission Vol. 44, Issue 11, Pages: 34-40(2020)
- 作者机构：
  
  1.北京交通大学电气工程学院，北京 100044
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2020.11.006
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况增平,佟庆彬,杜婧等.蛙跳算法优化品质因子的共振稀疏分解方法[J].机械传动,2020,44(11):34-40.

Kuang Zengping,Tong Qingbin,Du Jing,et al.Resonance Sparse Decomposition Method for Optimizing Quality Factor of Frog Leaping Algorithm[J].Journal of Mechanical Transmission,2020,44(11):34-40.
况增平,佟庆彬,杜婧等.蛙跳算法优化品质因子的共振稀疏分解方法[J].机械传动,2020,44(11):34-40. DOI： 10.16578/j.issn.1004.2539.2020.11.006.

Kuang Zengping,Tong Qingbin,Du Jing,et al.Resonance Sparse Decomposition Method for Optimizing Quality Factor of Frog Leaping Algorithm[J].Journal of Mechanical Transmission,2020,44(11):34-40. DOI： 10.16578/j.issn.1004.2539.2020.11.006.

摘要

由于滚动轴承在工程实际中的应用场景复杂多变，早期的滚动轴承故障容易被高强背景的噪声所掩盖，传统的故障特征提取方法效果不理想。针对现有共振稀疏分解方法的品质因子手动选取不严谨的问题，提出蛙跳算法优化参数模型，将品质因子进行蛙跳算法优化，再将得到的最优品质因子组合输入到共振稀疏分解模型中进行处理，得到低共振分量，将低共振分量进行Hilbert解调分析。设定低共振分量的峭度值最大为目标，进行蛙跳迭代寻优，最终实现了故障频率的识别。

Abstract

Due to the complex and varied application scenarios of rolling bearings in engineering practice， early rolling bearing failures are easily masked by high-intensity background noise， and traditional fault feature extraction methods are not ideal. Aiming at the rigorous manual selection of the quality factors of the existing resonance sparse decomposition method， a frog leaping algorithm optimization parameter model is proposed， and the quality factors are optimized by the frog leaping algorithm， then the optimal quality factor combination is input into the resonant sparse decomposition model for processing， the low resonance component is obtained， and the low resonance component is subjected to Hilbert demodulation analysis. The maximum kurtosis value of the low resonance component is set as the goal， and iterative optimization of frog leaping is performed to finally identify the fault frequency.

关键词

滚动轴承共振稀疏分解品质因子蛙跳算法

Keywords

Rolling bearingResonance sparse decompositionQuality factorFrog leaping algorithm

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