Advanced maintenance strategies for energy infrastructure: Lessons for optimizing rotating machinery

The reliability and efficiency of energy infrastructure depend heavily on the performance of rotating machinery, such as turbines, compressors, and pumps. Advanced maintenance strategies, particularly predictive and condition-based maintenance, have emerged as crucial approaches for optimizing the operational performance and lifespan of such machinery. This paper explores key lessons learned from implementing these advanced strategies in energy infrastructure, with a focus on how they can enhance system reliability, reduce downtime, and lower maintenance costs. By integrating digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML), maintenance can be more accurately forecasted, enabling preemptive action before machinery failure occurs. The role of real-time monitoring systems and the use of vibration analysis, thermal imaging, and acoustic monitoring as diagnostic tools are examined in detail. Additionally, the paper delves into the challenges associated with the implementation of these strategies, including the need for skilled personnel, the high costs of sensor deployment, and the integration of data from diverse sources. However, despite these challenges, case studies of energy firms that have adopted advanced maintenance strategies demonstrate significant improvements in operational efficiency and machinery uptime. Lessons from industries that rely on rotating machinery, such as oil and gas, power generation, and renewable energy sectors, are highlighted, underscoring the importance of continuous innovation in maintenance techniques. The paper concludes by providing recommendations for organizations looking to optimize their rotating machinery, including prioritizing the deployment of predictive maintenance technologies, fostering collaboration between maintenance and IT departments, and continuously training personnel in the latest diagnostic tools. Ultimately, advanced maintenance strategies present a pathway for energy companies to improve asset management, enhance sustainability, and minimize the risk of unexpected failures.

Authors

Publication Details

Journal
Global journal of research in science and technology
Published
2024-10-24
DOI
https://doi.org/10.58175/gjrst.2024.2.2.0073
Citations
5
Primary Topic
Oil and Gas Production Techniques
Type
article
Field-Weighted Citation Impact
1.68
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Advanced maintenance strategies for energy infrastructure: Lessons for optimizing rotating machinery

Enobong Hanson, Adeoye Taofik Aderamo, Chukwuebuka Nwakile, Oluwaseyi Ayotunde Akano et al.
5 citations
Global journal of research in science and technology
Oil and Gas Production Techniques
1.68
article

Advanced maintenance strategies for energy infrastructure: Lessons for optimizing rotating machinery

Enobong Hanson, Adeoye Taofik Aderamo, Chukwuebuka Nwakile, Oluwaseyi Ayotunde Akano, Ovie Vincent Erhueh
article en
5 citations

Abstract

The reliability and efficiency of energy infrastructure depend heavily on the performance of rotating machinery, such as turbines, compressors, and pumps. Advanced maintenance strategies, particularly predictive and condition-based maintenance, have emerged as crucial approaches for optimizing the operational performance and lifespan of such machinery. This paper explores key lessons learned from implementing these advanced strategies in energy infrastructure, with a focus on how they can enhance system reliability, reduce downtime, and lower maintenance costs. By integrating digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML), maintenance can be more accurately forecasted, enabling preemptive action before machinery failure occurs. The role of real-time monitoring systems and the use of vibration analysis, thermal imaging, and acoustic monitoring as diagnostic tools are examined in detail. Additionally, the paper delves into the challenges associated with the implementation of these strategies, including the need for skilled personnel, the high costs of sensor deployment, and the integration of data from diverse sources. However, despite these challenges, case studies of energy firms that have adopted advanced maintenance strategies demonstrate significant improvements in operational efficiency and machinery uptime. Lessons from industries that rely on rotating machinery, such as oil and gas, power generation, and renewable energy sectors, are highlighted, underscoring the importance of continuous innovation in maintenance techniques. The paper concludes by providing recommendations for organizations looking to optimize their rotating machinery, including prioritizing the deployment of predictive maintenance technologies, fostering collaboration between maintenance and IT departments, and continuously training personnel in the latest diagnostic tools. Ultimately, advanced maintenance strategies present a pathway for energy companies to improve asset management, enhance sustainability, and minimize the risk of unexpected failures.

Global journal of research in science and technologyVol. 2(2)
Industry, innovation and infrastructure
Openalex Percentile: Top 17%
Oil and Gas Production Techniques
1.68
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Advanced maintenance strategies for energy infrastructure: Lessons for optimizing rotating machinery — Enobong Hanson, Adeoye Taofik Aderamo, et al. · Global journal of research in science and technology (2024) | TGRS Research Map | TGRS