# Bearing fault diagnosis based on multi-scale spectral images and convolutional neural network

arXiv: https://arxiv.org/abs/2503.21566 · HTML: https://mlsanity.com/paper/2503.21566/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 27 Mar 2025.

## Builds on (most cited first, 3 of 3)

- [Data-driven Machinery Fault Diagnosis: A Comprehensive Review](https://mlsanity.com/paper/2405.18843/) (2024)
- [Multi-Fault Diagnosis Of Industrial Rotating Machines Using Data-Driven Approach: A Review Of Two Decades Of Research](https://mlsanity.com/paper/2206.14153/) (2022)
- [Bearing fault diagnosis based on spectrum images of vibration signals](https://mlsanity.com/paper/1511.02503/) (2015)

## Abstract (arXiv)

To address the challenges of low diagnostic accuracy in traditional bearing fault diagnosis methods, this paper proposes a novel fault diagnosis approach based on multi-scale spectrum feature images and deep learning. Firstly, the vibration signal are preprocessed through mean removal and then converted to multi-length spectrum with fast Fourier transforms (FFT). Secondly, a novel feature called multi-scale spectral image (MSSI) is constructed by multi-length spectrum paving scheme. Finally, a deep learning framework, convolutional neural network (CNN), is formulated to diagnose the bearing faults. Two experimental cases are utilized to verify the effectiveness of the proposed method. Experimental results demonstrate that the proposed method significantly improves the accuracy of fault diagnosis.