HUST bearing: a practical dataset for ball bearing fault diagnosis
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.