Infrared Thermal Image-Based Sustainable Fault Detection for Electrical Facilities
Abstract
:1. Introduction
2. Background
2.1. Faster R-CNN
2.2. YOLOv3
3. Methods
3.1. Data Collection and Preprocessing
3.2. Object Detection
3.2.1. Structure of Faster R-CNN
3.2.2. Region Proposal Network (RPN)
- (i)
- The anchors with the highest IoU (Intersection-over-Union) overlap with a ground-truth box.
- (ii)
- An anchor that has an IoU overlap greater than 0.7 with any ground-truth box.
3.2.3. Structure of YOLOv3
3.2.4. Training of YOLOv3
3.3. Thermal Intensity Area Analysis
Algorithm 1. Thermal Intensity Area Analysis (TIAA) |
Input:: Temperature of detecting facility i |
Conditions (Emissivity , Air temperature , Relative humidity , Thermocouple) |
of facility i |
: Standard temperature of facility i |
Output: Alert with detecting facility’s conditions |
1. If > then |
2. Alert TIAA System |
3. Provide conditions () of facility i |
4. Experimental Results
4.1. Data Collection and Preprocessing
4.2. Object Detection Model Training and Evaluation
4.3. Thermal Intensity Area Analysis
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variable | Explanation |
---|---|
Predicted probability of anchor i being an object | |
Ground-truth label of whether anchor i is an object (1 or 0) | |
Vector representing the four parameterized coordinates of the predicted box | |
Vector representing the four coordinates of the ground-truth box | |
The batch size | |
The number of anchors | |
Classification log loss over two classes (object vs. not-object) | |
Regression loss | |
Balancing weight |
Material | Emissivity |
---|---|
Aluminum Weathered | 0.83 |
Copper Polished | 0.05 |
Copper Oxidized | 0.78 |
Nickel | 0.05 |
Polymeric rubber | 0.96 |
Stainless Steel Polished | 0.16 |
Stainless Steel Oxidized | 0.85 |
Steel Polished | 0.07 |
Steel Oxidized | 0.79 |
Measure | Faster R-CNN | YOLOv3 |
---|---|---|
Average precision (AP) of COS | 70.4% | 62.2% |
AP of CT | 55.7% | 38.1% |
AP of INS | 68.2% | 78.0% |
AP of LA | 61.2% | 19.1% |
Mean average precision (mAP) | 63.9% | 49.4% |
Frame per second (FPS) | 4 | 33 |
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Kim, J.S.; Choi, K.N.; Kang, S.W. Infrared Thermal Image-Based Sustainable Fault Detection for Electrical Facilities. Sustainability 2021, 13, 557. https://doi.org/10.3390/su13020557
Kim JS, Choi KN, Kang SW. Infrared Thermal Image-Based Sustainable Fault Detection for Electrical Facilities. Sustainability. 2021; 13(2):557. https://doi.org/10.3390/su13020557
Chicago/Turabian StyleKim, Ju Sik, Kyu Nam Choi, and Sung Woo Kang. 2021. "Infrared Thermal Image-Based Sustainable Fault Detection for Electrical Facilities" Sustainability 13, no. 2: 557. https://doi.org/10.3390/su13020557
APA StyleKim, J. S., Choi, K. N., & Kang, S. W. (2021). Infrared Thermal Image-Based Sustainable Fault Detection for Electrical Facilities. Sustainability, 13(2), 557. https://doi.org/10.3390/su13020557