基于双对抗编码的时变工况下行星齿轮箱智能故障诊断

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摘要时变工况下行星齿轮箱故障特征频率随时间变化，常规的统计特征通常难以有效地表征非平稳信号的时变特性，人工识别故障特征较为困难。针对上述问题，提出基于双对抗编码的智能故障诊断模型。先获取样本信号的时频图，揭示信号频率随时间变化规律；构建编码与解码网络，并用编码器的输入与解码器的输出对判别器 1 进行对抗训练，确保重构信号与原始信号服从相同分布，从而提取有效的时频图特征；此外，构建高斯混合分布，并根据类别信息从对应分布进行采样，判别器 2 用于使提取的特征服从给定的高斯混合分布，从而实现通过控制混合分布来强化不同类别特征间的差异性。最后，用强化的特征训练Softmax 分类器，并识别测试样本故障类别。方法经行星齿轮箱实验数据进行了验证，研究表明，模型通过对抗机制使重构信号服从与原始信号相同的分布，同时通过高斯混合分布对隐变量进行控制，提高了特征聚类性能，有效诊断了齿轮故障，与其他方法相比表现出一定的优越性。

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	赵川
	冯志鹏
	张颖琳
	王坤

关键词 ：行星齿轮箱, 智能故障诊断, 高斯混合分布, 双对抗编码, 时变工况

Abstract：Characteristic frequencies of planetary gearboxes are time-varying when they are running under time-varying condition, and conventional statistics are unsuitable to explore time variability and potential properties of nonstationary signals.These make fault diagnosis of a planetary gearbox manually difficult.In order to address these issues, a bilateral adversarial encoder model was proposed.Firstly, a time-frequency representation image of a sample was obtained to reveal the time-varying frequencies.Then, an encoder and a decoder were established.The input of the encoder and the output of the decoder were utilized to train discriminator 1 to ensure that the reconstructed signal follows the distribution of original signal, and the features of an image are effectively extracted.Besides, a Gaussian mixture distribution was established and samples were collected from the Gaussian mixture distribution according to the label information.Discriminator 2 is utilized to make the extracted features follow the distribution of the samples in order to enhance the discriminability of features among different classes by controlling the mixture distribution.Finally, a Softmax classifier was trained by the enhanced features and the testing features were identified.This method was validated via planetary gearbox data set.The results indicate that the reconstructed signal can be made by an adversarial game to follow the distribution of the original signal and the features can be controlled by a Gaussian mixture distribution to improve their clustering performance and diagnose the gear faults accurately.In comparison with other methods, it works better to some degree.

Key words： planetary gearbox intelligent fault diagnosis Gaussian mixture distribution bilateral adversarial encoder time-varying condition

收稿日期: 2020-05-18 出版日期: 2021-10-28

引用本文:

赵川，冯志鹏，张颖琳，王坤. 基于双对抗编码的时变工况下行星齿轮箱智能故障诊断[J]. 振动与冲击, 2021, 40(20): 158-167.
ZHAO Chuan,FENG Zhipeng,ZHANG Yinglin,WANG Kun. Intelligent fault diagnosis of planetary gearboxes under time-varying condition based on bilateral adversarial encoder. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(20): 158-167.

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http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2021/V40/I20/158

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