基于ACMPE、ISSL-Isomap和GWO-SVM的行星齿轮箱故障诊断

戚晓利; 王振亚; 吴保林; 叶绪丹; 潘紫微

doi:10.13224/j.cnki.jasp.2019.04.002

基于ACMPE、ISSL-Isomap和GWO-SVM的行星齿轮箱故障诊断

doi: 10.13224/j.cnki.jasp.2019.04.002

安徽工业大学机械工程学院，安徽马鞍山 243032

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)

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出版历程
- 收稿日期: 2018-07-24
- 刊出日期: 2019-04-28

Planetary gearbox fault diagnosis based on ACMPE, ISSL-Isomap and GWO-SVM

摘要

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。
Abstract: 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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