Advancements in predictive maintenance for aging oil and gas infrastructure
Fuente:
Zenodo
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Recurso digital |
| Lenguaje: | inglés |
| Publicado: |
Zenodo
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866901888181469184 |
|---|---|
| author | Olusile Akinyele Babayeju Adedayo Adefemi Ifeanyi Onyedika Ekemezie Oludayo Olatoye Sofoluwe |
| author_facet | Olusile Akinyele Babayeju Adedayo Adefemi Ifeanyi Onyedika Ekemezie Oludayo Olatoye Sofoluwe |
| contents | <p>The oil and gas industry relies heavily on aging infrastructure to extract, transport, and process hydrocarbons. As these assets age, the risk of failures and downtime increases, leading to safety hazards and costly repairs. Predictive maintenance has emerged as a valuable strategy to mitigate these risks by using data-driven insights to predict equipment failures and schedule maintenance proactively. This review highlights advancements in predictive maintenance technologies for aging oil and gas infrastructure, focusing on the benefits and challenges of implementation. Advancements in sensor technology and data analytics have significantly improved the effectiveness of predictive maintenance in the oil and gas industry. Sensors installed on critical equipment collect real-time data on temperature, pressure, vibration, and other key parameters, providing insights into equipment health and performance. Data analytics tools analyze this data to identify patterns and trends indicative of potential failures, enabling operators to take preventive action before a breakdown occurs. Machine learning algorithms have also played a crucial role in enhancing predictive maintenance capabilities. These algorithms can process large volumes of data and learn from past equipment failures to predict future issues accurately. By continuously learning from new data, machine learning algorithms can improve their predictive accuracy over time, leading to more effective maintenance strategies. Despite these advancements, implementing predictive maintenance in aging oil and gas infrastructure poses several challenges. One major challenge is integrating new sensor technology with existing equipment, which may require retrofitting or upgrading existing assets. Another challenge is managing the vast amounts of data generated by sensors and analytics tools, which can strain existing IT infrastructure and require specialized expertise to analyze effectively. In conclusion, advancements in predictive maintenance technologies offer significant benefits for aging oil and gas infrastructure. By leveraging sensor technology, data analytics, and machine learning, operators can predict equipment failures, reduce downtime, and extend the life of critical assets. However, implementing these technologies requires careful planning and investment to overcome challenges related to integration, data management, and expertise.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14725645 |
| institution | Zenodo |
| language | eng |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Advancements in predictive maintenance for aging oil and gas infrastructure Olusile Akinyele Babayeju Adedayo Adefemi Ifeanyi Onyedika Ekemezie Oludayo Olatoye Sofoluwe Advancement Predictive Maintenance Aging Oil Gas Infrastructur <p>The oil and gas industry relies heavily on aging infrastructure to extract, transport, and process hydrocarbons. As these assets age, the risk of failures and downtime increases, leading to safety hazards and costly repairs. Predictive maintenance has emerged as a valuable strategy to mitigate these risks by using data-driven insights to predict equipment failures and schedule maintenance proactively. This review highlights advancements in predictive maintenance technologies for aging oil and gas infrastructure, focusing on the benefits and challenges of implementation. Advancements in sensor technology and data analytics have significantly improved the effectiveness of predictive maintenance in the oil and gas industry. Sensors installed on critical equipment collect real-time data on temperature, pressure, vibration, and other key parameters, providing insights into equipment health and performance. Data analytics tools analyze this data to identify patterns and trends indicative of potential failures, enabling operators to take preventive action before a breakdown occurs. Machine learning algorithms have also played a crucial role in enhancing predictive maintenance capabilities. These algorithms can process large volumes of data and learn from past equipment failures to predict future issues accurately. By continuously learning from new data, machine learning algorithms can improve their predictive accuracy over time, leading to more effective maintenance strategies. Despite these advancements, implementing predictive maintenance in aging oil and gas infrastructure poses several challenges. One major challenge is integrating new sensor technology with existing equipment, which may require retrofitting or upgrading existing assets. Another challenge is managing the vast amounts of data generated by sensors and analytics tools, which can strain existing IT infrastructure and require specialized expertise to analyze effectively. In conclusion, advancements in predictive maintenance technologies offer significant benefits for aging oil and gas infrastructure. By leveraging sensor technology, data analytics, and machine learning, operators can predict equipment failures, reduce downtime, and extend the life of critical assets. However, implementing these technologies requires careful planning and investment to overcome challenges related to integration, data management, and expertise.</p> |
| title | Advancements in predictive maintenance for aging oil and gas infrastructure |
| topic | Advancement Predictive Maintenance Aging Oil Gas Infrastructur |
| url | https://doi.org/10.5281/zenodo.14725645 |