@article{10.11648/j.acis.20241202.11,
  author = {Obi Chukwuemeka Nwokonkwo and Nwankwo Uchechukwu Samuel and Udoka Felista Eze and Adetokunbo MacGregor John-Otumu},
  title = {Machine Learning Framework for Real-Time Pipeline Anomaly Detection and Maintenance Needs Forecast Using Random Forest and Prophet Model
},
  journal = {Automation, Control and Intelligent Systems},
  volume = {12},
  number = {2},
  pages = {22-34},
  doi = {10.11648/j.acis.20241202.11},
  url = {https://doi.org/10.11648/j.acis.20241202.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20241202.11},
  abstract = {This paper introduces an Intelligent Model for Real-Time Pipeline Monitoring and Maintenance Prediction to enhance infrastructure integrity and operational efficiency in Nigeria's oil and gas sector. Given the country's economic dependence on oil and gas revenue, efficient pipeline transportation is crucial. However, pipelines face challenges such as corrosion, mechanical failures, vandalism, and theft, leading to economic losses and environmental risks. Current monitoring systems are mainly reactive, lacking timely anomaly detection and predictive maintenance capabilities. To tackle these challenges, the study utilized sophisticated machine learning methods by combining the Random Forest classifier for real-time anomaly detection with the Prophet model for predictive maintenance forecasting. Datasets from Kaggle were used. The Random Forest classifier demonstrated robust performance with an accuracy of 93.48%, precision of 93.75%, recall of 96.77%, and an F1-score of 95.24%. The Prophet model provided accurate hourly forecasts of operational parameters, aiding proactive maintenance scheduling. Despite some errors encountered (RMSE: 21.48 and MAE: 18.17), the Mean Absolute Percentage Error (MAPE) of 14.87% indicates relatively minor discrepancies compared to actual values. In conclusion, the Intelligent Model shows significant advancements in pipeline monitoring and maintenance prediction by leveraging machine learning for early anomaly detection and timely maintenance interventions. This proactive approach aims to reduce downtime, prevent environmental damage, and optimize operational efficiency in Nigeria's oil and gas infrastructure. Future research could focus on enhancing system scalability across diverse terrains, employing advanced deep learning techniques such as Transformer Networks and Autoencoders for improved prediction accuracy, and exploring cybersecurity measures like blockchain integration to ensure data integrity and protect critical infrastructure from cyber threats.
},
 year = {2024}
}

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TY  - JOUR
T1  - Machine Learning Framework for Real-Time Pipeline Anomaly Detection and Maintenance Needs Forecast Using Random Forest and Prophet Model

AU  - Obi Chukwuemeka Nwokonkwo
AU  - Nwankwo Uchechukwu Samuel
AU  - Udoka Felista Eze
AU  - Adetokunbo MacGregor John-Otumu
Y1  - 2024/07/31
PY  - 2024
N1  - https://doi.org/10.11648/j.acis.20241202.11
DO  - 10.11648/j.acis.20241202.11
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 22
EP  - 34
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20241202.11
AB  - This paper introduces an Intelligent Model for Real-Time Pipeline Monitoring and Maintenance Prediction to enhance infrastructure integrity and operational efficiency in Nigeria's oil and gas sector. Given the country's economic dependence on oil and gas revenue, efficient pipeline transportation is crucial. However, pipelines face challenges such as corrosion, mechanical failures, vandalism, and theft, leading to economic losses and environmental risks. Current monitoring systems are mainly reactive, lacking timely anomaly detection and predictive maintenance capabilities. To tackle these challenges, the study utilized sophisticated machine learning methods by combining the Random Forest classifier for real-time anomaly detection with the Prophet model for predictive maintenance forecasting. Datasets from Kaggle were used. The Random Forest classifier demonstrated robust performance with an accuracy of 93.48%, precision of 93.75%, recall of 96.77%, and an F1-score of 95.24%. The Prophet model provided accurate hourly forecasts of operational parameters, aiding proactive maintenance scheduling. Despite some errors encountered (RMSE: 21.48 and MAE: 18.17), the Mean Absolute Percentage Error (MAPE) of 14.87% indicates relatively minor discrepancies compared to actual values. In conclusion, the Intelligent Model shows significant advancements in pipeline monitoring and maintenance prediction by leveraging machine learning for early anomaly detection and timely maintenance interventions. This proactive approach aims to reduce downtime, prevent environmental damage, and optimize operational efficiency in Nigeria's oil and gas infrastructure. Future research could focus on enhancing system scalability across diverse terrains, employing advanced deep learning techniques such as Transformer Networks and Autoencoders for improved prediction accuracy, and exploring cybersecurity measures like blockchain integration to ensure data integrity and protect critical infrastructure from cyber threats.

VL  - 12
IS  - 2
ER  - 

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