时变工况下行星轮轴承特征分布拟合与智能故障诊断

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振动与冲击 ›› 2021, Vol. 40 ›› Issue (14) : 252-260.

论文

赵川1，2，冯志鹏2

作者信息 +

Features distribution fitting and intelligent fault diagnosis of planet bearings under time-varying condition

ZHAO Chuan1,2， FENG Zhipeng2

Author information +

文章历史 +

摘要

行星齿轮箱中行星轮轴承运动复杂，振动信号成分多样且呈非线性，时变工况下特征频率随时间变化，人工识别故障特征更为困难。针对该问题，提出基于对抗变分自编码的智能故障诊断模型。获取样本时频图来揭示样本中包含的时变特征；利用模型中的变分自编码器自适应提取时频图特征，为赋予特征显式意义，引入多维独立高斯分布并进行采样，根据类别信息对样本点进行变换，使其服从新的多维独立高斯分布，确保样本点中的每个元素都有自己的专属分布；通过对抗机制，使特征逼近变换后的对应类别的分布样本，且服从新的多维独立高斯分布，从而实现用已知的分布拟合未知的特征分布，同时通过控制分布强化不同类别特征间的差异性，改善特征的模式识别性能；利用优化后的特征对分类器进行训练并识别测试样本特征。模型经行星齿轮箱实验台数据进行了验证。研究表明，该模型能够使提取的特征服从给定的先验分布，通过控制分布强化了不同类别特征间的差异性，提高了特征的聚合性能，有效诊断了行星轮轴承故障，与自编码和变分自编码器相比，表现出一定的优越性。

Abstract

The intricate kinematics of planet bearings in planetary gearboxes leads to complex and even non-linear vibration signals.Besides, the characteristic frequencies change with time under time-varying working condition, which makes the fault diagnosis of planet bearings manually more difficult.For addressing these issues, an intelligent fault diagnosis method was proposed based on the adversarial variational auto-encoder.Firstly, the time-frequency representation of a sample was obtained to reveal the time-varying properties.Then, the variational auto-encoder of the model was utilized to extract features of a time-frequency image.In order to make the latent features exposed in an explicit meaning, a multivariate independent Gaussian distribution was introduced, and samples were collected from the distribution.After that, the samples were transformed into new ones according to the label information and made to follow a new multivariate independent Gaussian distribution to ensure each element in the sample has its own distribution.Through an adversarial game, the latent features were made to have the same distribution as the corresponding transformed samples and follow the new distribution so that the unknown features distribution could be fitted by a given prior distribution.And the discriminability of the features among different classes was enhanced by controlling the distribution to improve their performance for pattern identification.Finally, a classifier was trained and tested by the optimized features.The method was validated via some planetary gearbox data sets.The results show that the model enables extracted features among different classes to follow an explicit distribution, improves their clustering performance for pattern identification and effectively diagnoses planet bearing faults.It outperforms to a certain extent the traditional auto-encoder and variational auto-encoder.

导出引用

赵川，冯志鹏. 时变工况下行星轮轴承特征分布拟合与智能故障诊断[J]. 振动与冲击, 2021, 40(14): 252-260

ZHAO Chuan， FENG Zhipeng. Features distribution fitting and intelligent fault diagnosis of planet bearings under time-varying condition[J]. Journal of Vibration and Shock, 2021, 40(14): 252-260

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