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An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition

机译：基于生成对抗神经网络的智能诊断方案及其在行星齿轮箱故障模式识别中的应用

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摘要

Planetary gearbox has complex structures and works under various non-stationary operating conditions. The vibration signals of planetary gearbox are complicated and usually polluted by noise and interference. It is difficult to extract effective features of early faults. In addition, there are only a small number of fault samples for planetary gearbox fault diagnosis. All of these increase the difficulty of planetary gearbox fault diagnosis. Aiming at these problems, a novel fault diagnostic method is proposed which combines Generative Adversarial Networks (GAN) and Stacked Denoising Autoencoders (SDAE). The generator of GAN can generate new samples which has similar distribution with original samples from planetary gearbox vibration signals. Then, generated samples are transformed to the discriminator together with original samples which expand the sample size. SDAE is used as the discriminator of GAN which can automatically extract effective fault features from input samples and discriminate their authenticity and fault categories. Through novel adversarial machine learning mechanism, the generator and discriminator are concurrently optimized to enhance the quality of generation samples and the ability of fault mode classification. The experimental results show that the developed SDAE-GAN method for planetary gearbox has good anti-noise ability and achieve better fault diagnosis performance in the case of small samples. (C) 2018 Elsevier B.V. All rights reserved.
机译：行星齿轮箱结构复杂，可在各种非平稳运行条件下工作。行星齿轮箱的振动信号比较复杂，通常会受到噪声和干扰的污染。很难提取早期故障的有效特征。此外，只有少量的故障样本可用于行星齿轮箱故障诊断。所有这些增加了行星齿轮箱故障诊断的难度。针对这些问题，提出了一种新的故障诊断方法，该方法结合了生成对抗网络（GAN）和堆叠降噪自动编码器（SDAE）。 GAN的生成器可以从行星齿轮箱振动信号中生成与原始样本具有相似分布的新样本。然后，将生成的样本与原始样本一起转换为鉴别器，从而扩大了样本大小。 SDAE用作GAN的鉴别器，可以自动从输入样本中提取有效的故障特征，并区分其真实性和故障类别。通过新颖的对抗机器学习机制，同时优化了生成器和鉴别器，以提高生成样本的质量和故障模式分类的能力。实验结果表明，所开发的用于行星齿轮箱的SDAE-GAN方法具有良好的抗噪声能力，在小样本情况下具有较好的故障诊断性能。 （C）2018 Elsevier B.V.保留所有权利。

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