Summary of Thermal Image-based Fault Diagnosis in Induction Machines Via Self-organized Operational Neural Networks, by Sertac Kilickaya et al.
Thermal Image-based Fault Diagnosis in Induction Machines via Self-Organized Operational Neural Networks
by Sertac Kilickaya, Cansu Celebioglu, Levent Eren, Murat Askar
First submitted to arxiv on: 8 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
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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 This paper presents an innovative approach to condition monitoring of induction machines using thermal imaging and Self-Organized Operational Neural Networks (Self-ONNs). The authors aim to diagnose misalignment and broken rotor faults in squirrel-cage induction motors, which are common issues encountered in industrial environments. To evaluate their method, they benchmark it against various Convolutional Neural Network (CNN) models, including ResNet, EfficientNet, PP-LCNet, SEMNASNet, and MixNet, using a Workswell InfraRed Camera (WIC). The results show that Self-ONNs achieve comparable diagnostic performance to more complex CNN models while utilizing a shallower architecture with just three operational layers. This streamlined architecture makes it well-suited for deployment on edge devices, enabling its use in more complex multi-function and/or multi-device monitoring systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special cameras to look at the temperature of machines to help figure out if they are working properly or not. They want to know if there is a problem with how the machine is aligned or if something inside the machine has broken. They use special computer programs called Self-Organized Operational Neural Networks (Self-ONNs) to try and solve this problem. They compare their method to other methods that people have tried before, using lots of different cameras and computers. The results show that their way works just as well as some of the more complicated ways, but it is simpler and easier to use. |
Keywords
» Artificial intelligence » Cnn » Neural network » Resnet » Temperature
