# Deep learning-based fault identification in condition monitoring

arXiv: https://arxiv.org/abs/2410.05889 · HTML: https://mlsanity.com/paper/2410.05889/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 8 Oct 2024.

## Builds on (most cited first, 4 of 4)

- [Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better](https://mlsanity.com/paper/2106.08962/) (2021)
- [Imaging Time-Series to Improve Classification and Imputation](https://mlsanity.com/paper/1506.00327/) (2015)
- [A Hybrid Method for Condition Monitoring and Fault Diagnosis of Rolling Bearings With Low System Delay](https://mlsanity.com/paper/2208.06051/) (2022)
- [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)

## Built on by (most cited first, 1 of 1)

- [YOLO-Based Pipeline Monitoring in Challenging Visual Environments](https://mlsanity.com/paper/2507.02967/) (2025)

## Abstract (arXiv)

Vibration-based condition monitoring techniques are commonly used to identify faults in rolling element bearings. Accuracy and speed of fault detection procedures are critical performance measures in condition monitoring. Delay is especially important in remote condition monitoring and time-sensitive industrial applications. While most existing methods focus on accuracy, little attention has been given to the inference time in the fault identification process. In this paper, we address this gap by presenting a Convolutional Neural Network (CNN) based approach for real-time fault identification in rolling element bearings. We encode raw vibration signals into two-dimensional images using various encoding methods and use these with a CNN to classify several categories of bearing fault types and sizes. We analyse the interplay between fault identification accuracy and processing time. For training and evaluation we use a bearing failure CWRU dataset.