Research on Time Series-Based Pipeline Ground Penetrating Radar Calibration Angle Prediction Algorithm
The pipeline ground-penetrating radar stands as an indispensable detection device for ensuring underground space security. A wheeled pipeline robot is deployed to traverse the interior of urban underground drainage pipelines along their central axis. It is subject to influences such as resistance, s...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/2/379 |
| _version_ | 1797339410735300608 |
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| author | Maoxuan Xu Feng Yang Yuanjin Fang Fanruo Li Rui Yan |
| author_facet | Maoxuan Xu Feng Yang Yuanjin Fang Fanruo Li Rui Yan |
| author_sort | Maoxuan Xu |
| collection | DOAJ |
| description | The pipeline ground-penetrating radar stands as an indispensable detection device for ensuring underground space security. A wheeled pipeline robot is deployed to traverse the interior of urban underground drainage pipelines along their central axis. It is subject to influences such as resistance, speed, and human factors, leading to deviations in its posture. A guiding wheel is employed to rectify its roll angle and ensure the precise spatial positioning of defects both inside and outside the pipeline, as detected by the radar antenna. By analyzing its deflection factors and correction trajectories, the intelligent correction control of the pipeline ground-penetrating radar falls into the realm of nonlinear multi-constraint optimization. Consequently, a time-series-based correction angle prediction algorithm is proposed. The application of the long short-term memory (LSTM) deep learning model facilitates the prediction of correction angles and torque for the guiding wheel. This study compares the performance of LSTM with an autoregressive integrated moving average model under identical dataset conditions. The subsequent findings reveal a reduction of 4.11° and 8.25 N·m in mean absolute error, and a decrease of 10.66% and 7.27% in mean squared error for the predicted correction angles and torques, respectively. These outcomes are achieved utilizing the three-channel drainage pipeline ground-penetrating radar device with top antenna operating at 1.2 GHz and left/right antennas at 750 MHz. The LSTM prediction model intelligently corrects its posture. Experimental results demonstrate an average correction speed of 5 s and an average angular error of ±1°. It is verified that the model can correct its attitude in real-time with small errors, thereby enhancing the accuracy of ground-penetrating radar antennas in locating pipeline defects. |
| first_indexed | 2024-03-08T09:47:44Z |
| format | Article |
| id | doaj.art-646b064ddf974f729153a0d152ab085b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T09:47:44Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-646b064ddf974f729153a0d152ab085b2024-01-29T14:13:55ZengMDPI AGSensors1424-82202024-01-0124237910.3390/s24020379Research on Time Series-Based Pipeline Ground Penetrating Radar Calibration Angle Prediction AlgorithmMaoxuan Xu0Feng Yang1Yuanjin Fang2Fanruo Li3Rui Yan4School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, ChinaSchool of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, ChinaSchool of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, ChinaSchool of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, ChinaBeijing Drainage Group Co., Ltd., Beijing 100044, ChinaThe pipeline ground-penetrating radar stands as an indispensable detection device for ensuring underground space security. A wheeled pipeline robot is deployed to traverse the interior of urban underground drainage pipelines along their central axis. It is subject to influences such as resistance, speed, and human factors, leading to deviations in its posture. A guiding wheel is employed to rectify its roll angle and ensure the precise spatial positioning of defects both inside and outside the pipeline, as detected by the radar antenna. By analyzing its deflection factors and correction trajectories, the intelligent correction control of the pipeline ground-penetrating radar falls into the realm of nonlinear multi-constraint optimization. Consequently, a time-series-based correction angle prediction algorithm is proposed. The application of the long short-term memory (LSTM) deep learning model facilitates the prediction of correction angles and torque for the guiding wheel. This study compares the performance of LSTM with an autoregressive integrated moving average model under identical dataset conditions. The subsequent findings reveal a reduction of 4.11° and 8.25 N·m in mean absolute error, and a decrease of 10.66% and 7.27% in mean squared error for the predicted correction angles and torques, respectively. These outcomes are achieved utilizing the three-channel drainage pipeline ground-penetrating radar device with top antenna operating at 1.2 GHz and left/right antennas at 750 MHz. The LSTM prediction model intelligently corrects its posture. Experimental results demonstrate an average correction speed of 5 s and an average angular error of ±1°. It is verified that the model can correct its attitude in real-time with small errors, thereby enhancing the accuracy of ground-penetrating radar antennas in locating pipeline defects.https://www.mdpi.com/1424-8220/24/2/379underground space securitypipeline penetrating radar robotdeflection angle predictionintelligent deflection correctionlong short-term memory neural networks |
| spellingShingle | Maoxuan Xu Feng Yang Yuanjin Fang Fanruo Li Rui Yan Research on Time Series-Based Pipeline Ground Penetrating Radar Calibration Angle Prediction Algorithm Sensors underground space security pipeline penetrating radar robot deflection angle prediction intelligent deflection correction long short-term memory neural networks |
| title | Research on Time Series-Based Pipeline Ground Penetrating Radar Calibration Angle Prediction Algorithm |
| title_full | Research on Time Series-Based Pipeline Ground Penetrating Radar Calibration Angle Prediction Algorithm |
| title_fullStr | Research on Time Series-Based Pipeline Ground Penetrating Radar Calibration Angle Prediction Algorithm |
| title_full_unstemmed | Research on Time Series-Based Pipeline Ground Penetrating Radar Calibration Angle Prediction Algorithm |
| title_short | Research on Time Series-Based Pipeline Ground Penetrating Radar Calibration Angle Prediction Algorithm |
| title_sort | research on time series based pipeline ground penetrating radar calibration angle prediction algorithm |
| topic | underground space security pipeline penetrating radar robot deflection angle prediction intelligent deflection correction long short-term memory neural networks |
| url | https://www.mdpi.com/1424-8220/24/2/379 |
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