| 丛晓,李根.基于CYCBD和麻雀搜索算法的滚动轴承故障特征提取方法[J].装备环境工程,2022,19(8):36-41. CONG Xiao,LI Gen.Fault Feature Extraction Method of Rolling Bearing Based on CYCBD and Sparrow Search Algorithm[J].Equipment Environmental Engineering,2022,19(8):36-41. |
| 基于CYCBD和麻雀搜索算法的滚动轴承故障特征提取方法 |
| Fault Feature Extraction Method of Rolling Bearing Based on CYCBD and Sparrow Search Algorithm |
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| DOI：10.7643/issn.1672-9242.2022.08.006 |
| 中文关键词: 滚动轴承 故障特征提取 麻雀搜索算法 CYCBD 滚转尾翼导弹 强噪声中图分类号:TP206 文献标识码:A 文章编号:1672-9242(2022)08-0036-06 |
| 英文关键词:rolling bearing fault feature extraction sparrow search algorithm CYCBD rolling tail missile strong noise |
| 基金项目:国家自然科学基金(51975580) |
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| 中文摘要: |
| 目的 解决在较强的噪声环境下最大二阶循环平稳盲解卷积(Maximum Second Order Cyclostationary Blind Deconvolution，CYCBD)算法在滚动轴承故障特征提取时效果欠佳的问题，为滚转尾翼导弹的尾翼滚动轴承故障诊断提供方法参考。方法 提出一种利用麻雀搜索算法(Sparrow Search Algorithm，SSA)优化CYCBD算法的方法，将CYCBD算法解卷积的包络谱熵作为SSA寻优的适应度函数，利用SSA高效地寻找出合适的循环频率以及滤波器长度，选择自适应参数后，再使用CYCBD算法有效解卷得到周期脉冲特征。结果 同时对比SSA优化CYCBD前后进行故障特征提取的包络谱图，CYCBD的噪声幅值不超过0.13 m/s2，峰值不超过0.29 m/s2，用SSA优化CYCBD的噪声幅值不超过0.08 m/s2，峰值不超过0.32 m/s2，故障频率成分更加突显，无论是噪声幅度，还是峰值幅度特性，均较CYCBD有了较大改善。结论 仿真实验验证了SSA优化CYCBD方法能够更加清晰地辨识到故障特征频率及其倍频成分，其具有良好的工程应用前景。 |
| 英文摘要: |
| The paper aims to solve the problem that the effect of maximum second-order cyclostationary blind deconvolution (CYCBD) algorithm in rolling bearing fault feature extraction is not good in strong noise environment, and provide a method reference for rolling bearing fault diagnosis of rolling tail missile. A method using sparrow search algorithm (SSA) to optimize CYCBD algorithm is proposed. The envelope spectral entropy of deconvolution of CYCBD algorithm is taken as the fitness function of SSA optimization. The appropriate cycle frequency and filter length are efficiently found by SSA. After adaptive parameter selection, CYCBD algorithm is used to effectively deconvolute to obtain periodic pulse characteristics. At the same time, the envelope spectrum of fault feature extraction before and after SSA optimization CYCBD is compared. The noise amplitude of CYCBD is not more than 0.13 m/s2, and the peak value is not more than 0.29 m/s2. The noise amplitude of CYCBD optimized by SSA is not more than 0.08 m/s2, and the peak value is not more than 0.32 m/s2. The fault frequency component is more prominent, and the noise amplitude and peak amplitude characteristics are greatly improved compared with CYCBD. The simulation results verify that the SSA optimized CYCBD method can more clearly identify the fault characteristic frequency and its frequency doubling components, and it has a good engineering application prospect. |
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