Deep learning-based fault identification in condition monitoring
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916427892523008 |
|---|---|
| author | Dhungana, Hariom Mukhiya, Suresh Kumar Dhungana, Pragya Karic, Benjamin |
| author_facet | Dhungana, Hariom Mukhiya, Suresh Kumar Dhungana, Pragya Karic, Benjamin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05889 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep learning-based fault identification in condition monitoring Dhungana, Hariom Mukhiya, Suresh Kumar Dhungana, Pragya Karic, Benjamin Machine Learning Artificial Intelligence 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. |
| title | Deep learning-based fault identification in condition monitoring |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2410.05889 |