A Machine-Learning-Based and IoT-Enabled Robot Swarm System for Pipeline Crack Detection
In today’s expanding cities, pipeline networks are becoming an essential part of the industrial infrastructure. Monitoring these pipelines autonomously is becoming increasingly important. Inspecting pipelines for cracks is one specific task that poses a huge burden on humans. Undetected cracks may p...
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MDPI AG
2024-12-01
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| Series: | IoT |
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| Online Access: | https://www.mdpi.com/2624-831X/5/4/43 |
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| author | Ayman Kandil Mounib Khanafer Ali Darwiche Reem Kassem Fatima Matook Ahmad Younis Habib Badran Maryam Bin-Jassem Ossama Ahmed Ali Behiry Mohammed El-Abd |
| author_facet | Ayman Kandil Mounib Khanafer Ali Darwiche Reem Kassem Fatima Matook Ahmad Younis Habib Badran Maryam Bin-Jassem Ossama Ahmed Ali Behiry Mohammed El-Abd |
| author_sort | Ayman Kandil |
| collection | DOAJ |
| description | In today’s expanding cities, pipeline networks are becoming an essential part of the industrial infrastructure. Monitoring these pipelines autonomously is becoming increasingly important. Inspecting pipelines for cracks is one specific task that poses a huge burden on humans. Undetected cracks may pose multi-dimensional risks. In this paper, we introduce the Pipeline Leak Identification Emergency Robot Swarm (PLIERS) system, an industrial system that deploys Internet-of-Things (IoT), robotics, and neural network technologies to detect cracks in emptied water and sewage pipelines. In PLIERS, a swarm of robots inspect emptied pipelines from the inside to detect cracks, collect images of them, and register their locations. When the images are taken, they are fed into a cloud-based module for analysis by a convolutional neural network (CNN). The CNN is used to detect cracks and identify their severity. Through extensive training and testing, the CNN model performance showed promising scores for accuracy (between 80% and 90%), recall (at least 95%), precision (at least 95%), and F1 (at least 96%). Additionally, through the careful design of a prototype for a water/sewage pipeline structure with several types of cracks, the robots used managed to exchange information among themselves and convey crack images to the cloud-based server for further analysis. PLIERS is a system that deploys modern technologies to detect and recognize cracks in pipeline grids. It adds to the efforts of improving instrumentation and measurement approaches by using robots, sensory, IoT principles, and the efficient analysis of CNNs. |
| format | Article |
| id | doaj-art-fd7ede5d2f86489ca2ce85248812c203 |
| institution | DOAJ |
| issn | 2624-831X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | IoT |
| spelling | doaj-art-fd7ede5d2f86489ca2ce85248812c2032025-08-20T02:53:38ZengMDPI AGIoT2624-831X2024-12-015495196910.3390/iot5040043A Machine-Learning-Based and IoT-Enabled Robot Swarm System for Pipeline Crack DetectionAyman Kandil0Mounib Khanafer1Ali Darwiche2Reem Kassem3Fatima Matook4Ahmad Younis5Habib Badran6Maryam Bin-Jassem7Ossama Ahmed8Ali Behiry9Mohammed El-Abd10Department of Computer Science and Engineering, American University of Sharjah, University City, Sharjah P.O. Box 26666, United Arab EmiratesCollege of Engineering and Applied Sciences, American University of Kuwait, P.O. Box 3323, Safat 13034, KuwaitCollege of Engineering and Applied Sciences, American University of Kuwait, P.O. Box 3323, Safat 13034, KuwaitCollege of Engineering and Applied Sciences, American University of Kuwait, P.O. Box 3323, Safat 13034, KuwaitCollege of Engineering and Applied Sciences, American University of Kuwait, P.O. Box 3323, Safat 13034, KuwaitCollege of Engineering and Applied Sciences, American University of Kuwait, P.O. Box 3323, Safat 13034, KuwaitCollege of Engineering and Applied Sciences, American University of Kuwait, P.O. Box 3323, Safat 13034, KuwaitCollege of Engineering and Applied Sciences, American University of Kuwait, P.O. Box 3323, Safat 13034, KuwaitCollege of Engineering and Applied Sciences, American University of Kuwait, P.O. Box 3323, Safat 13034, KuwaitCollege of Engineering and Applied Sciences, American University of Kuwait, P.O. Box 3323, Safat 13034, KuwaitCollege of Engineering and Applied Sciences, American University of Kuwait, P.O. Box 3323, Safat 13034, KuwaitIn today’s expanding cities, pipeline networks are becoming an essential part of the industrial infrastructure. Monitoring these pipelines autonomously is becoming increasingly important. Inspecting pipelines for cracks is one specific task that poses a huge burden on humans. Undetected cracks may pose multi-dimensional risks. In this paper, we introduce the Pipeline Leak Identification Emergency Robot Swarm (PLIERS) system, an industrial system that deploys Internet-of-Things (IoT), robotics, and neural network technologies to detect cracks in emptied water and sewage pipelines. In PLIERS, a swarm of robots inspect emptied pipelines from the inside to detect cracks, collect images of them, and register their locations. When the images are taken, they are fed into a cloud-based module for analysis by a convolutional neural network (CNN). The CNN is used to detect cracks and identify their severity. Through extensive training and testing, the CNN model performance showed promising scores for accuracy (between 80% and 90%), recall (at least 95%), precision (at least 95%), and F1 (at least 96%). Additionally, through the careful design of a prototype for a water/sewage pipeline structure with several types of cracks, the robots used managed to exchange information among themselves and convey crack images to the cloud-based server for further analysis. PLIERS is a system that deploys modern technologies to detect and recognize cracks in pipeline grids. It adds to the efforts of improving instrumentation and measurement approaches by using robots, sensory, IoT principles, and the efficient analysis of CNNs.https://www.mdpi.com/2624-831X/5/4/43automationconvolutional neural networkindustrial applicationinstrumentation and measurementInternet of Thingspipeline crack detection |
| spellingShingle | Ayman Kandil Mounib Khanafer Ali Darwiche Reem Kassem Fatima Matook Ahmad Younis Habib Badran Maryam Bin-Jassem Ossama Ahmed Ali Behiry Mohammed El-Abd A Machine-Learning-Based and IoT-Enabled Robot Swarm System for Pipeline Crack Detection IoT automation convolutional neural network industrial application instrumentation and measurement Internet of Things pipeline crack detection |
| title | A Machine-Learning-Based and IoT-Enabled Robot Swarm System for Pipeline Crack Detection |
| title_full | A Machine-Learning-Based and IoT-Enabled Robot Swarm System for Pipeline Crack Detection |
| title_fullStr | A Machine-Learning-Based and IoT-Enabled Robot Swarm System for Pipeline Crack Detection |
| title_full_unstemmed | A Machine-Learning-Based and IoT-Enabled Robot Swarm System for Pipeline Crack Detection |
| title_short | A Machine-Learning-Based and IoT-Enabled Robot Swarm System for Pipeline Crack Detection |
| title_sort | machine learning based and iot enabled robot swarm system for pipeline crack detection |
| topic | automation convolutional neural network industrial application instrumentation and measurement Internet of Things pipeline crack detection |
| url | https://www.mdpi.com/2624-831X/5/4/43 |
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