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

arXiv: https://arxiv.org/abs/2302.12533 · HTML: https://mlsanity.com/paper/2302.12533/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 54 references that are in the map.
- First posted on arXiv 24 Feb 2023.

## Builds on (most cited first, 5 of 5)

- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [Revisiting Batch Normalization For Practical Domain Adaptation](https://mlsanity.com/paper/1603.04779/) (2016)
- [Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study](https://mlsanity.com/paper/1912.12528/) (2019)
- [A comprehensive review on convolutional neural network in machine fault diagnosis](https://mlsanity.com/paper/2002.07605/) (2020)
- [A Hybrid Instance-based Transfer Learning Method](https://mlsanity.com/paper/1812.01063/) (2018)

## Built on by (most cited first, 9 of 9)

- [Towards a more realistic evaluation of machine learning models for bearing fault diagnosis](https://mlsanity.com/paper/2509.22267/) (2025)
- [Neuronal Attention Circuit (NAC) for Representation Learning](https://mlsanity.com/paper/2512.10282/) (2025)
- [Towards a Universal Vibration Analysis Dataset: A Framework for Transfer Learning in Predictive Maintenance and Structural Health Monitoring](https://mlsanity.com/paper/2504.11581/) (2025)
- [Developing Distance-Aware Physics-Constrained Probabilistic Frameworks for Industrial Prognostics](https://mlsanity.com/paper/2512.08499/) (2025)
- [FLUID: Continuous-Time Hyperconnected Sparse Transformer for Sink-Free Learning](https://mlsanity.com/paper/2605.04421/) (2026)
- [Neuronal Stochastic Attention Circuit (NSAC) for Probabilistic Representation Learning](https://mlsanity.com/paper/2605.26061/) (2026)
- [Active Foundational Models for Fault Diagnosis of Electrical Motors](https://mlsanity.com/paper/2311.15516/) (2023)
- [Compound Fault Diagnosis for Train Transmission Systems Using Deep Learning with Fourier-enhanced Representation](https://mlsanity.com/paper/2504.07155/) (2025)
- [When Labels Are Scarce: An Oscillatory State Space Model for Vibration Diagnosis](https://mlsanity.com/paper/2609.27411/) (2026)

## Abstract (arXiv)

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.