Data-driven multi-fault detection in pipelines utilizing frequency response function and artificial neural networks
This research presents a data-driven structural health monitoring (SHM) approach for pipeline systems that leverages frequency response function (FRF) signals and artificial neural network (ANN) algorithms to accurately identify and classify diverse pipeline fault conditions. The study focuses on th...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2025-03-01
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| Series: | Journal of Pipeline Science and Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667143324000507 |
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| author | Hussein A. M. Hussein Sharafiz B. Abdul Rahim Faizal B. Mustapha Prajindra S. Krishnan Nawal Aswan B. Abdul Jalil |
| author_facet | Hussein A. M. Hussein Sharafiz B. Abdul Rahim Faizal B. Mustapha Prajindra S. Krishnan Nawal Aswan B. Abdul Jalil |
| author_sort | Hussein A. M. Hussein |
| collection | DOAJ |
| description | This research presents a data-driven structural health monitoring (SHM) approach for pipeline systems that leverages frequency response function (FRF) signals and artificial neural network (ANN) algorithms to accurately identify and classify diverse pipeline fault conditions. The study focuses on three specific faults: bolt looseness, scale deposits, and crack occurrence at pipeline supports, which were replicated on a pipeline segment located at the Sound and Vibration Research Group (SVRG) at University Putra Malaysia (UPM). The FRF signals were captured using accelerometers to monitor the structural health of the pipeline. The data acquisition stage involved collecting FRF signals from the accelerometers to capture vibrations and responses related to the identified faults using a Siemens LMS SCADAS data acquisition unit. The data underwent preprocessing, including the application of principal component analysis (PCA) for feature selection. The subsequent data processing stage involved the application of an ANN algorithm for pattern recognition to analyze and classify the acquired data, identifying patterns associated with the replicated fault conditions. The proposed methodology demonstrated exceptional performance, with the ANN model achieving consistently high overall accuracy (above 99.7%) and remarkably low mean squared error (in the range of 0.0088 × 10−3 to 0.3062 × 10−3) across multiple iterations and sensor datasets. The detailed class-specific metrics, including accuracy, precision, sensitivity, and F1-score, further substantiated the model’s effectiveness in identifying the individual fault types with near-perfect or perfect results for the majority of the fault scenarios. The location-invariant performance of the ANN model across different sensor placements demonstrates the robustness of the proposed data-driven SHM methodology. This research highlights the transformative potential of integrating state-of-the-art data-driven techniques to revolutionize the monitoring and assessment of critical pipeline infrastructure, ultimately enhancing the safety, reliability, and longevity of these vital systems. |
| format | Article |
| id | doaj-art-572934091a614acd90a4730e9775805d |
| institution | Kabale University |
| issn | 2667-1433 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Journal of Pipeline Science and Engineering |
| spelling | doaj-art-572934091a614acd90a4730e9775805d2025-01-30T05:15:10ZengKeAi Communications Co. Ltd.Journal of Pipeline Science and Engineering2667-14332025-03-0151100223Data-driven multi-fault detection in pipelines utilizing frequency response function and artificial neural networksHussein A. M. Hussein0Sharafiz B. Abdul Rahim1Faizal B. Mustapha2Prajindra S. Krishnan3Nawal Aswan B. Abdul Jalil4Universiti Putra Malaysia (UPM), Jalan Universiti 1, Serdang, Selangor 43400, Malaysia; Waha Oil Company, Burj Boleila, El Shat Street, Tripoli, LibyaUniversiti Putra Malaysia (UPM), Jalan Universiti 1, Serdang, Selangor 43400, Malaysia; Corresponding author.Universiti Putra Malaysia (UPM), Jalan Universiti 1, Serdang, Selangor 43400, MalaysiaUniversiti Tenaga Nasional (UNITEN), Jalan IKRAM-UNITEN, Kajang, Selangor 43000, MalaysiaUniversiti Putra Malaysia (UPM), Jalan Universiti 1, Serdang, Selangor 43400, MalaysiaThis research presents a data-driven structural health monitoring (SHM) approach for pipeline systems that leverages frequency response function (FRF) signals and artificial neural network (ANN) algorithms to accurately identify and classify diverse pipeline fault conditions. The study focuses on three specific faults: bolt looseness, scale deposits, and crack occurrence at pipeline supports, which were replicated on a pipeline segment located at the Sound and Vibration Research Group (SVRG) at University Putra Malaysia (UPM). The FRF signals were captured using accelerometers to monitor the structural health of the pipeline. The data acquisition stage involved collecting FRF signals from the accelerometers to capture vibrations and responses related to the identified faults using a Siemens LMS SCADAS data acquisition unit. The data underwent preprocessing, including the application of principal component analysis (PCA) for feature selection. The subsequent data processing stage involved the application of an ANN algorithm for pattern recognition to analyze and classify the acquired data, identifying patterns associated with the replicated fault conditions. The proposed methodology demonstrated exceptional performance, with the ANN model achieving consistently high overall accuracy (above 99.7%) and remarkably low mean squared error (in the range of 0.0088 × 10−3 to 0.3062 × 10−3) across multiple iterations and sensor datasets. The detailed class-specific metrics, including accuracy, precision, sensitivity, and F1-score, further substantiated the model’s effectiveness in identifying the individual fault types with near-perfect or perfect results for the majority of the fault scenarios. The location-invariant performance of the ANN model across different sensor placements demonstrates the robustness of the proposed data-driven SHM methodology. This research highlights the transformative potential of integrating state-of-the-art data-driven techniques to revolutionize the monitoring and assessment of critical pipeline infrastructure, ultimately enhancing the safety, reliability, and longevity of these vital systems.http://www.sciencedirect.com/science/article/pii/S2667143324000507Pipeline structural health monitoring (SHM)Fault detectionArtificial neural networks (ANNS)Frequency response function (FRF)Principal component analysis (PCA)Bolt looseness |
| spellingShingle | Hussein A. M. Hussein Sharafiz B. Abdul Rahim Faizal B. Mustapha Prajindra S. Krishnan Nawal Aswan B. Abdul Jalil Data-driven multi-fault detection in pipelines utilizing frequency response function and artificial neural networks Journal of Pipeline Science and Engineering Pipeline structural health monitoring (SHM) Fault detection Artificial neural networks (ANNS) Frequency response function (FRF) Principal component analysis (PCA) Bolt looseness |
| title | Data-driven multi-fault detection in pipelines utilizing frequency response function and artificial neural networks |
| title_full | Data-driven multi-fault detection in pipelines utilizing frequency response function and artificial neural networks |
| title_fullStr | Data-driven multi-fault detection in pipelines utilizing frequency response function and artificial neural networks |
| title_full_unstemmed | Data-driven multi-fault detection in pipelines utilizing frequency response function and artificial neural networks |
| title_short | Data-driven multi-fault detection in pipelines utilizing frequency response function and artificial neural networks |
| title_sort | data driven multi fault detection in pipelines utilizing frequency response function and artificial neural networks |
| topic | Pipeline structural health monitoring (SHM) Fault detection Artificial neural networks (ANNS) Frequency response function (FRF) Principal component analysis (PCA) Bolt looseness |
| url | http://www.sciencedirect.com/science/article/pii/S2667143324000507 |
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