Remaining Useful Life Prediction for Pressurized Fluid Pipelines Based on Acoustic Emission Monitoring and an Adaptive Fuzzy Similarity Measure
Pressurized fluid pipelines are among the most crucial components in industrial settings. Operating under high pressure leads to pipeline susceptibility to cracking, rupture, and significant damage. Monitoring the condition and predicting the remaining useful life before the failure of pressurized p...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10614577/ |
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| author | Duc-Thuan Nguyen Tuan-Khai Nguyen Zahoor Ahmad Jong-Myon Kim |
| author_facet | Duc-Thuan Nguyen Tuan-Khai Nguyen Zahoor Ahmad Jong-Myon Kim |
| author_sort | Duc-Thuan Nguyen |
| collection | DOAJ |
| description | Pressurized fluid pipelines are among the most crucial components in industrial settings. Operating under high pressure leads to pipeline susceptibility to cracking, rupture, and significant damage. Monitoring the condition and predicting the remaining useful life before the failure of pressurized pipes are essential for informed and timely maintenance decisions. In this work, we propose a novel method for predicting the remaining useful life of pressurized pipelines based on acoustic emission monitoring and similarity-based learning. Specifically, acoustic emission sensors are deployed to record acoustic emission events caused by cracks in the pipeline. A pipeline health indicator is proposed based on accumulated events detected through a constant false alarm rate signal detector. Leveraging the historical run-to-fail trajectories of the health indicator, a similarity measure is introduced to predict the remaining pipeline life. This method computes the similarity between the current health indicator trajectory and past trajectories based on a Euclidean distance in the proposed derivative convolutional domain. Trajectory similarity determines the remaining lifetime similarity, which is weighted using data-adaptive fuzzy rules to estimate the current remaining useful life. Elaborate experimental validations are conducted on a custom pressurized pipeline system in a laboratory setting. Experimental results demonstrate the high efficacy of the proposed method in predicting the remaining useful life of the pipeline, surpassing other commonly used methods in both accuracy and certainty. |
| format | Article |
| id | doaj-art-0b2f451b54ae42ddbbed2590937562a8 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-0b2f451b54ae42ddbbed2590937562a82025-08-20T03:19:20ZengIEEEIEEE Access2169-35362024-01-011210451810453210.1109/ACCESS.2024.343569410614577Remaining Useful Life Prediction for Pressurized Fluid Pipelines Based on Acoustic Emission Monitoring and an Adaptive Fuzzy Similarity MeasureDuc-Thuan Nguyen0https://orcid.org/0000-0002-5749-5409Tuan-Khai Nguyen1https://orcid.org/0000-0001-8999-6745Zahoor Ahmad2https://orcid.org/0000-0002-3571-8907Jong-Myon Kim3https://orcid.org/0000-0002-5185-1062Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South KoreaDepartment of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South KoreaDepartment of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South KoreaDepartment of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South KoreaPressurized fluid pipelines are among the most crucial components in industrial settings. Operating under high pressure leads to pipeline susceptibility to cracking, rupture, and significant damage. Monitoring the condition and predicting the remaining useful life before the failure of pressurized pipes are essential for informed and timely maintenance decisions. In this work, we propose a novel method for predicting the remaining useful life of pressurized pipelines based on acoustic emission monitoring and similarity-based learning. Specifically, acoustic emission sensors are deployed to record acoustic emission events caused by cracks in the pipeline. A pipeline health indicator is proposed based on accumulated events detected through a constant false alarm rate signal detector. Leveraging the historical run-to-fail trajectories of the health indicator, a similarity measure is introduced to predict the remaining pipeline life. This method computes the similarity between the current health indicator trajectory and past trajectories based on a Euclidean distance in the proposed derivative convolutional domain. Trajectory similarity determines the remaining lifetime similarity, which is weighted using data-adaptive fuzzy rules to estimate the current remaining useful life. Elaborate experimental validations are conducted on a custom pressurized pipeline system in a laboratory setting. Experimental results demonstrate the high efficacy of the proposed method in predicting the remaining useful life of the pipeline, surpassing other commonly used methods in both accuracy and certainty.https://ieeexplore.ieee.org/document/10614577/Remaining useful lifepipelinesacoustic emissionfuzzy logicsimilarity learning |
| spellingShingle | Duc-Thuan Nguyen Tuan-Khai Nguyen Zahoor Ahmad Jong-Myon Kim Remaining Useful Life Prediction for Pressurized Fluid Pipelines Based on Acoustic Emission Monitoring and an Adaptive Fuzzy Similarity Measure IEEE Access Remaining useful life pipelines acoustic emission fuzzy logic similarity learning |
| title | Remaining Useful Life Prediction for Pressurized Fluid Pipelines Based on Acoustic Emission Monitoring and an Adaptive Fuzzy Similarity Measure |
| title_full | Remaining Useful Life Prediction for Pressurized Fluid Pipelines Based on Acoustic Emission Monitoring and an Adaptive Fuzzy Similarity Measure |
| title_fullStr | Remaining Useful Life Prediction for Pressurized Fluid Pipelines Based on Acoustic Emission Monitoring and an Adaptive Fuzzy Similarity Measure |
| title_full_unstemmed | Remaining Useful Life Prediction for Pressurized Fluid Pipelines Based on Acoustic Emission Monitoring and an Adaptive Fuzzy Similarity Measure |
| title_short | Remaining Useful Life Prediction for Pressurized Fluid Pipelines Based on Acoustic Emission Monitoring and an Adaptive Fuzzy Similarity Measure |
| title_sort | remaining useful life prediction for pressurized fluid pipelines based on acoustic emission monitoring and an adaptive fuzzy similarity measure |
| topic | Remaining useful life pipelines acoustic emission fuzzy logic similarity learning |
| url | https://ieeexplore.ieee.org/document/10614577/ |
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