HUST bearing: a practical dataset for ball bearing fault diagnosis
This provides a practical dataset for researchers and engineers working on bearing fault diagnosis, but it is incremental as it focuses on data collection and initial evaluation rather than novel methods.
The authors introduced the HUST bearing dataset, a large collection of vibration data for ball bearing fault diagnosis, and evaluated it using classical machine learning and unsupervised transfer learning methods, achieving up to 100% accuracy in classification and 60-80% in transfer learning tasks.
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.