School of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Korea.
Sensors (Basel). 2020 May 31;20(11):3105. doi: 10.3390/s20113105.
The vibration signals of gearbox gear fault signatures are informative components that can be used for gearbox fault diagnosis and early fault detection. However, the vibration signals are normally non-linear and non-stationary, and they contain background noise caused by data acquisition systems and the interference of other machine elements. Especially in conditions with varying rotational speeds, the informative components are blended with complex, unwanted components inside the vibration signal. Thus, to use the informative components from a vibration signal for gearbox fault diagnosis, the noise needs to be properly distilled from the informational signal as much as possible before analysis. This paper proposes a novel gearbox fault diagnosis method based on an adaptive noise reducer-based Gaussian reference signal (ANR-GRS) technique that can significantly reduce noise and improve classification from a one-against-one, multiclass support vector machine (OAOMCSVM) for the fault types of a gearbox. The ANR-GRS processes the shaft rotation speed to access and remove noise components in the narrowbands between two consecutive sideband frequencies along the frequency spectrum of a vibration signal, enabling the removal of enormous noise components with minimal distortion to the informative signal. The optimal output signal from the ANR-GRS is then extracted into many signal feature vectors to generate a qualified classification dataset. Finally, the OAOMCSVM classifies the health states of an experimental gearbox using the dataset of extracted features. The signal processing and classification paths are generated using the experimental testbed. The results indicate that the proposed method is reliable for fault diagnosis in a varying rotational speed gearbox system.
齿轮箱齿轮故障特征的振动信号是有用的组成部分，可用于齿轮箱故障诊断和早期故障检测。然而，振动信号通常是非线性和非平稳的，并且它们包含数据采集系统引起的背景噪声和其他机器元件的干扰。特别是在转速变化的情况下，有用的组成部分与振动信号中的复杂、不需要的组成部分混合在一起。因此，为了使用振动信号中的有用组成部分进行齿轮箱故障诊断，在进行分析之前，需要尽可能从信息信号中适当提取噪声。本文提出了一种基于自适应噪声消减基于高斯参考信号（ANR-GRS）技术的新型齿轮箱故障诊断方法，该方法可以显著减少噪声，并提高基于一对一、多类支持向量机（OAOMCSVM）的分类性能，用于齿轮箱的故障类型。ANR-GRS 处理轴转速，以访问并去除振动信号频谱中两个连续边带频率之间的窄带中的噪声成分，从而可以去除大量噪声成分，而对信息信号的失真最小。然后，从 ANR-GRS 中提取最优输出信号到许多信号特征向量中，以生成合格的分类数据集。最后，使用提取的特征数据集，通过 OAOMCSVM 对实验齿轮箱的健康状态进行分类。信号处理和分类路径是使用实验测试平台生成的。结果表明，该方法对于变速齿轮箱系统的故障诊断是可靠的。
