Automation in Operations and Maintenance: Best Practices and Case Studies

The rapid evolution of automation technologies is revolutionising operations and maintenance (O&M) across industries. This study explores the fundamentals of automation in O&M, highlighting key technologies such as Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT). These technologies are transforming O&M practices through predictive maintenance, anomaly detection, real-time monitoring, and remote operations, leading to enhanced efficiency, cost savings, safety, and data-driven decision-making. The framework for intelligent O&M integrates machine-level optimisation, production-line interaction, and factory-level monitoring, leveraging real-time data and advanced analytics to improve resource allocation and system reliability. Challenges such as high implementation costs, integration with legacy systems, cybersecurity risks, and a lack of skilled personnel are examined. Case studies illustrate automation's impact in diverse domains. Offshore wind farms, where O&M accounts for up to 23% of lifecycle costs, benefit from innovations like self-elevating platforms and remote diagnostics. In agriculture, drones are optimising tasks like land monitoring and pesticide application, enhancing efficiency and reducing labour costs. This paper concludes with best practices for implementing automation technologies, focusing on dynamic resource allocation, real-time monitoring, and strategic decision-making. It underscores the need for continued innovation and investment to overcome adoption barriers and maximise automation's potential in O&M.