# FaultNet: A Deep Convolutional Neural Network for bearing fault classification

arXiv: https://arxiv.org/abs/2010.02146 · HTML: https://mlsanity.com/paper/2010.02146/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [FaultFormer: Pretraining Transformers for Adaptable Bearing Fault Classification](https://mlsanity.com/paper/2312.02380/) (2023)
- [Explainable fault and severity classification for rolling element bearings using Kolmogorov-Arnold networks](https://mlsanity.com/paper/2412.01322/) (2024)
- [DKDL-Net: A Lightweight Bearing Fault Detection Model via Decoupled Knowledge Distillation and Low-Rank Adaptation Fine-tuning](https://mlsanity.com/paper/2406.06653/) (2024)
- [Deep convolutional neural networks for cyclic sensor data](https://mlsanity.com/paper/2308.06987/) (2023)
- [Multimodal Bearing Fault Classification Under Variable Conditions: A 1D CNN with Transfer Learning](https://mlsanity.com/paper/2502.17524/) (2025)
- [LSR-Net: A Lightweight and Strong Robustness Network for Bearing Fault Diagnosis in Noise Environment](https://mlsanity.com/paper/2601.10761/) (2026)

## Abstract (arXiv)

The increased presence of advanced sensors on the production floors has led to the collection of datasets that can provide significant insights into machine health. An important and reliable indicator of machine health, vibration signal data can provide us a greater understanding of different faults occurring in mechanical systems. In this work, we analyze vibration signal data of mechanical systems with bearings by combining different signal processing methods and coupling them with machine learning techniques to classify different types of bearing faults. We also highlight the importance of using different signal processing methods and analyze their effect on accuracy for bearing fault detection. Apart from the traditional machine learning algorithms we also propose a convolutional neural network FaultNet which can effectively determine the type of bearing fault with a high degree of accuracy. The distinguishing factor of this work is the idea of channels proposed to extract more information from the signal, we have stacked the Mean and Median channels to raw signal to extract more useful features to classify the signals with greater accuracy.