School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China.
Sensors (Basel). 2020 Mar 18;20(6):1685. doi: 10.3390/s20061685.
Planetary gearbox is a critical component for many mechanical systems. It is essential to monitor the planetary gearbox health and performance in order to maintain the whole machine works well. The methodology of mechanical fault diagnosis is increasingly intelligent with the extensive application of deep learning. However, the cross-domain issue caused by varying working conditions becomes an enormous encumbrance to fault diagnosis based on deep learning. In this paper, in order to fully excavate potentialities of deep neural network architectures, a novel generative adversarial learning method was introduced for a completely new fault diagnosis based on a deep convolution neural network. In addition, the intelligent fault diagnostic scheme for planetary gearbox under varying speed conditions was developed. After that, some experiments on measured vibration signals of planetary gearbox were conducted to verify the validity and efficiency of the fault diagnostic scheme. The results showed that the proposed method enhanced the capability of the intelligent diagnosis for planetary gear faults under varying speed conditions.
行星齿轮箱是许多机械系统的关键部件。为了保持整个机器的良好运行，监测行星齿轮箱的健康状况和性能至关重要。随着深度学习的广泛应用，机械故障诊断方法变得越来越智能化。然而，由于工作条件的变化而导致的跨域问题成为基于深度学习的故障诊断的巨大障碍。在本文中，为了充分挖掘深度神经网络结构的潜力，引入了一种新颖的生成对抗学习方法，用于基于深度卷积神经网络的全新故障诊断。此外，还开发了变速工况下行星齿轮箱的智能故障诊断方案。之后，对行星齿轮箱的实测振动信号进行了一些实验，以验证故障诊断方案的有效性和效率。结果表明，所提出的方法提高了变速工况下行星齿轮故障智能诊断的能力。
