基于RTSMFE、M-KRCDA与COA-SVM的行星齿轮箱故障诊断

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摘要针对从行星齿轮箱非线性、非平稳振动信号中提取故障特征困难的问题，提出了一种基于精细时移多尺度模糊熵（Refined Time-shift Multiscale Fuzzy Entropy, RTSMFE）、马氏距离的核正则化共面判别分析（Mahalanobis-Kernel Regularized Coplanar Discriminant Analysis, M-KRCDA）以及郊狼优化算法优化支持向量机（coyote optimization algorithm-support vector machine,COA-SVM）的行星齿轮箱故障诊断方法。首先利用RTSMFE计算和组合行星齿轮箱原始故障信号的特征向量，构建原始高维故障特征集；然后采用M-KRCDA的特征筛选方法，减少了特征的维数并提高特征故障识别的准确性和效率；最后将低维特征输入到COA-SVM进行故障类型的判别。行星齿轮箱故障诊断实验结果分析表明：所提方法能够准确识别行星齿轮箱的常见故障，具有一定的应用前景。
关键词：故障诊断；行星齿轮箱；精细时移多尺度模糊熵（RTSMFE）；马氏距离的核正则化共面判别分析（M-KRCDA）；郊狼优化算法优化支持向量机（COA-SVM）

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	戚晓利
	崔创创
	杨艳
	程主梓
	陈旭

关键词 ：故障诊断, 行星齿轮箱, 精细时移多尺度模糊熵（RTSMFE）, 马氏距离的核正则化共面判别分析（M-KRCDA）, 郊狼优化算法优化支持向量机（COA-SVM）

Abstract：Aiming at the problem of difficulty in extracting fault characteristics from the nonlinear and non-stationary vibration signals of the planetary gearbox, a fault diagnosis method for the planetary gearbox based on Refined Time-shift Multiscale Fuzzy Entropy(RTSMFE), Mahalanobis-Kernel Regularized Coplanar Discriminant Analysis (M-KRCDA) and Coyote optimization algorithm optimization support vector machine(COA-SVM) was proposed. First, RTSMFE is used to calculate and combine the feature vector of the original fault signal of the planetary gearbox to construct the original high-dimensional fault feature set. Then the M-KRCDA method is used for feature screening, which reduces the dimension of features and improves the accuracy and efficiency of feature fault recognition. Finally, COA-SVM is used to identify low-dimensional fault features. The analysis of the experimental results of the planetary gearbox fault diagnosis shows that the method proposed can accurately identify the common faults of the planetary gearbox and has certain application prospects.
Keywords: fault diagnosis; planetary gearbox; Refined Time-shift Multiscale Fuzzy Entropy(RTSMFE); Mahalanobis-Kernel Regularized Coplanar Discriminant Analysis (M-KRCDA); Coyote Optimization Algorithm optimization support vector machine (COA-SVM)

Key words： fault diagnosis planetary gearbox Refined Time-shift Multiscale Fuzzy Entropy(RTSMFE) Mahalanobis-Kernel Regularized Coplanar Discriminant Analysis (M-KRCDA) Coyote Optimization Algorithm optimization support vector machine (COA-SVM)

收稿日期: 2021-06-02 出版日期: 2022-11-15

引用本文:

戚晓利，崔创创，杨艳，程主梓，陈旭. 基于RTSMFE、M-KRCDA与COA-SVM的行星齿轮箱故障诊断[J]. 振动与冲击, 2022, 41(21): 109-120.
QI Xiaoli, CUI Chuangchuang, YANG Yan, CHENG Zhuzi, CHEN Xu. Planetary gearbox fault diagnosis based on RTSMFE, M-KRCDA and COA-SVM. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(21): 109-120.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I21/109

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