Summary of Deep Learning-based Fault Identification in Condition Monitoring, by Hariom Dhungana et al.
Deep learning-based fault identification in condition monitoring
by Hariom Dhungana, Suresh Kumar Mukhiya, Pragya Dhungana, Benjamin Karic
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract presents a novel Convolutional Neural Network (CNN) based approach for real-time fault identification in rolling element bearings using vibration-based condition monitoring techniques. The proposed method encodes raw vibration signals into two-dimensional images and utilizes the CNN to classify various bearing fault types and sizes. The paper analyzes the interplay between fault identification accuracy and processing time, highlighting the importance of inference speed in remote condition monitoring and time-sensitive industrial applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This innovative approach uses Convolutional Neural Networks (CNNs) to quickly identify faults in bearings using vibration signals. The method converts the signals into images that can be analyzed by a CNN to determine the type and size of the fault. This helps improve accuracy while also being fast enough for real-time monitoring. |
Keywords
» Artificial intelligence » Cnn » Inference » Neural network
