With the development of machine learning, a data-driven model has been widely used in vibration signal fault diagnosis. Most data-driven machine learning algorithms are built based on well-designed features, but feature extraction is usually required to be completed in advance. In the deep learning era, feature extraction and classifier learning are conducted simultaneously, which will lead to an end-to-end learning system. This paper explores which one of the two key factors, i.e., feature extraction and classification algorithm, is more essential for a specific task of vibration signal diagnosis during a learning system is generated. Feature extractions from vibration signal based on both well-known Gaussian model and statistical characteristics are discussed, respectively. And several classification algorithms are selected to experimentally validate the comparative impact of both feature extraction and classification algorithm on prediction performance.

翻译：随着机器学习的发展,数据驱动模型被广泛用于振动信号故障诊断,大多数数据驱动机器学习算法都是基于设计良好的特征,但通常需要事先完成特征提取。在深层次的学习时代,特征提取和分类学同时进行,这将导致一个端到端学习系统。本文探讨了两个关键因素之一,即特征提取和分类算法,对于在学习系统期间进行振动信号诊断的具体任务来说,哪一个更为重要。分别讨论了基于众所周知的高斯模型和统计特征的震动信号的精华提取。并选择了几个分类算法,以实验方式验证特征提取和分类算法对预测性能的比较影响。