USING DATA SCIENCE AND ARTIFICIAL INTELLIGENCE TO ENABLE TECHNOLOGY-DRIVEN OFFSHORE OPPORTUNITIES

摘要

Recent developments in data science are enabling new opportunities for marine and offshore operators to adopt a more effective asset management strategy. The crux of this strategy is to combine data analytics with maintenance records and operational experience to reduce unplanned downtime. This case study focuses on the assimilation and utilization of diverse and mostly unstructured data, which up until now was largely untapped in the marine and offshore industries. The information extracted from such sources is used to identify key trends in equipment reliability and to improve the understanding of assets' conditions. Such insights are particularly useful for marine and offshore operators in making critical decisions relating to machinery: optimal resource allocation; proactive planning for planned maintenance events and maximizing overall asset availability. From a Classification Society's perspective such as American Bureau of Shipping (ABS), this allows operators and/or owners to derive Class-based benefits like Condition-Based Maintenance (CBM).