| 引用本文: | 高先磊,赵佰亭,贾晓芬.基于 OCSSA 优化 VMD 的滚动轴承故障诊断方法(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2025，42（6）：41-47 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 目的 为解决目前滚动轴承故障特征提取困难和故障诊断准确率低等问题,提出了基于优化麻雀搜索算法,
即变分模态分解的方法,高效提取滚动轴承故障特征。 方法 首先,将麻雀搜索算法改进为融合鱼鹰和柯西变异的
麻雀搜索算法(Osprey-Cauchy-Sparrow Search Algorithm, OCSSA);其次,利用 OCSSA 优化 VMD(Variational Mode
Decomposition)参数来对轴承信号进行分解;最后,将特征向量作为卷积神经网络-双向长短记忆网络( CNNBiLSTM)的输入,进行了滚动轴承故障类型的识别。 结果 实验结果表明:基于 OCSSA-VMD 特征提取的诊断模型
的故障诊断准确率为 99. 333%,与麻雀搜索算法-VMD、灰狼优化算法-VMD、粒子群优化算法-VMD、传统 VMD 特
征提取方法相比,故障诊断准确率分别提高了 3. 666%、5%、 6. 667% 、9%。 结论 该方法充分地提取了故障特征,
大大提高了故障诊断准确率。 |
| 关键词: 轴承故障 特征提取 融合鱼鹰和柯西变异的麻雀搜索算法 CNN-BiLSTM |
| DOI： |
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| Fault Diagnosis Method of Rolling Bearings Based on OCSSA Optimizing VMD |
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GAO Xianlei ZHAO Baiting JIA Xiaofen
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School of Electrical and Information Engineering Anhui University of Science and Technology Huainan 232001 Anhui
China
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| Abstract: |
| Objective To address the challenges of extracting fault features and achieving accurate fault diagnosis for
rolling bearings a method based on the optimized sparrow search algorithm variational mode decomposition VMD was
proposed to efficiently extract rolling bearing fault features. Methods Firstly the sparrow search algorithm is improved by
integrating the Osprey and Cauchy mutation resulting in the Osprey-Cauchy-Sparrow Search Algorithm OCSSA .
Secondly the OCSSA was used to optimize the parameters of VMD for decomposing bearing signals. Finally the feature
vectors were input into a convolutional neural network-bidirectional long short-term memory network CNN-BiLSTM for
identifying rolling bearing fault types. Results Experimental results showed that the fault diagnosis accuracy of the
diagnostic model based on OCSSA-VMD feature extraction was 99. 333%. Compared with the Sparrow Search AlgorithmVMD Grey Wolf Optimizer-VMD Particle Swarm Optimization-VMD and traditional VMD feature extraction methods
the fault diagnosis accuracy of the proposed method was improved by 3. 666% 5% 6. 667% and 9% respectively.
Conclusion This method effectively captures fault features and significantly enhances the fault diagnosis accuracy. |
| Key words: bearing failure feature extraction sparrow search algorithm fusing osprey and Cauchy variation CNNBiLSTM |
