In this work, we introduce a practical dataset named HUST bearing, that provides a large set of vibration data on different ball bearings. This dataset contains 90 raw vibration data of 6 types of defects (inner crack, outer crack, ball crack, and their 2-combinations) on 5 types of bearing at 3 working conditions with the sample rate of 51,200 samples per second. We established the envelope analysis and order tracking analysis on the introduced dataset to allow an initial evaluation of the data. A number of classical machine learning classification methods are used to identify bearing faults of the dataset using features in different domains. The typical advanced unsupervised transfer learning algorithms also perform to observe the transferability of knowledge among parts of the dataset. The experimental results of examined methods on the dataset gain divergent accuracy up to 100% on classification task and 60-80% on unsupervised transfer learning task.

翻译：本文介绍了一个名为HUST轴承的实用数据集，该数据集提供了不同滚珠轴承的大量振动数据。该数据集包含5种类型轴承在3种工作条件下的90组原始振动数据，涵盖6种缺陷类型（内圈裂纹、外圈裂纹、滚珠裂纹及其两两组合），采样率为51,200样本/秒。我们对所引入的数据集进行了包络分析和阶次跟踪分析，以初步评估数据质量。利用不同域的特征，采用多种经典机器学习分类方法对数据集的轴承故障进行识别。典型的高级无监督迁移学习算法也被用于观察数据集各部分间知识的可迁移性。经检验方法在数据集上的实验结果显示，分类任务准确率最高可达100%，无监督迁移学习任务准确率在60-80%之间。