A review of maritime equipment prognostics health management from a classification society perspective

With the development of digital technology, the maritime industry is under continuous digital transformation. For example, from manned engine room to control room and even to remotely controlled or autonomous ships. Maintenance has also changed from being a reactive or scheduled procedure to a predictive and proactive activity. Proactive maintenance relies on the condition monitoring (CM) of equipment. Condition Based Maintenance (CBM), Reliability Centered Maintenance (RCM) and Prognostics and Health Management (PHM) primarily focusing on three technical processes, namely condition monitoring, fault diagnosis and prognosis, and maintenance decision-making. Over the recent years, research has been on these topics through two main approaches: data-driven approaches and model-based approaches. Especially data-driven approaches with Deep Learning (DL) techniques have become a popular direction with successful implementation in different domains. Classification Societies are also developing new standards and methods with the industry to provide assurance of these emerging services. This paper presents a comprehensive review of state-to-art methods for PHM techniques for the past few years (2017-2023). First, a general introduction to different approaches with a focus on data-driven methods is presented. Subsequently, a detailed review of techniques applied to PHM is provided, where unique contributions, advantages, limitations and challenges are discussed. This is followed by a chapter that discusses the technical rules and standards from classification societies (DNV and ABS). The paper then explores the benefits, challenges, existing problems, and recommendations for PHM from the perspective of classification societies. Maritime stakeholders may find this article to be a valuable guide or reference for the development of such services.