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戚晓利,王振亚,吴保林,叶绪丹,潘紫微.基于ACMPE、ISSL-Isomap和GWO-SVM的行星齿轮箱故障诊断[J].航空动力学报,2019,(4):744~755
基于ACMPE、ISSL-Isomap和GWO-SVM的行星齿轮箱故障诊断
Planetary gearbox fault diagnosis based on ACMPE, ISSL-Isomap and GWO-SVM
投稿时间:2018-07-24  
DOI:10.13224/j.cnki.jasp.2019.04.002
中文关键词:  故障诊断;行星齿轮箱;自适应复合多尺度排列熵(ACMPE)  改进监督型自组织增量学习神经网络界标点等度规映射(ISSL-Isomap);灰狼群优化支持向量机(GWO-SVM)
英文关键词:fault diagnosis  planetary gearbox  adaptive composite multi-scale permutation entropy(ACMPE)  improved supervised self-organizing incremental neural network landmark isometric mapping (ISSL-Isomap)  grey wolf optimizer support vector machine(GWO-SVM)
基金项目:国家自然科学基金(51505002);安徽省自然科学基金(1808085ME152);安徽省高校自然科学研究重点项目(KJ2017 A053);研究生创新研究基金(2017012)
作者单位
戚晓利 安徽工业大学 机械工程学院安徽 马鞍山 243032 
王振亚 安徽工业大学 机械工程学院安徽 马鞍山 243032 
吴保林 安徽工业大学 机械工程学院安徽 马鞍山 243032 
叶绪丹 安徽工业大学 机械工程学院安徽 马鞍山 243032 
潘紫微 安徽工业大学 机械工程学院安徽 马鞍山 243032 
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中文摘要:
      针对从行星齿轮箱非线性、非平稳振动信号特征提取困难的问题,提出了一种基于自适应复合多尺度排列熵(ACMPE)、改进监督型自组织增量学习神经网络界标点等度规映射(ISSL-Isomap)和灰狼群优化支持向量机(GWO-SVM)相结合的行星齿轮箱故障诊断方法。利用ACMPE从复杂域提取振动信号的故障特征,构建高维故障特征集;采用ISSL-Isomap方法对高维故障特征集进行维数约简,提取出低维、敏感故障特征;应用GWO -SVM分类器对低维故障特征进行模式识别,判断故障类型。行星齿轮箱故障诊断实验结果分析表明:与多尺度排列熵(MPE)、复合多尺度排列熵(CMPE)等特征提取方法相比,ACMPE方法在分类效果和识别精度上更具优势;与局部切空间排列(LTSA)、等度规映射(Isomap)、加权Isomap(W-Isomap)、监督Isomap(S-Isomap)和监督型自组织增量学习神经网络界标点Isomap(SSL-Isomap)等降维方法进行比较,ISSL-Isomap方法降维效果最佳;所提方法的故障识别率达到100%,具有一定优越性。
英文摘要:
      In view of the difficulty of extracting nonlinear and non-stationary vibration signals from planetary gearboxes, a planetary gearbox fault diagnosis method based on adaptive composite multi-scale permutation entropy (ACMPE), improved supervised self-organizing incremental neural network landmark isometric mapping (ISSL-Isomap) and grey wolf optimizer support vector machine (GWO-SVM) was proposed. Fault features of vibration signals were extracted from the complex domain by using ACMPE, and the high-dimensional fault feature set was constructed. ISSL-Isomap was used to reduce the dimension of the high-dimensional fault feature set, and the low-dimensional and sensitive fault features were extracted. The low-dimensional fault features were input into a GWO-SVM classifier to recognize fault types. The analysis results of planetary gearbox fault diagnosis show that compared with the feature extraction methods of multi-scale permutation entropy (MPE) and composite MPE (CMPE), ACMPE has more advantages in classification effect and recognition accuracy. ISSL-Isomap has the best dimensionality reduction effect compared with the dimensionality reduction algorithms of local tangent space alignment (LTSA), isometric mapping (Isomap), weighted Isomap (W-Isomap), supervised Isomap (S-Isomap) and supervised self-organizing incremental neural network landmark Isomap (SSL-Isomap). The fault recognition rate of the proposed method reaches 100% with a certain superiority.
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