Fault detection and diagnostics (FDD) have great potential to enable safety, efficiency, and reliability measures of critical machinery systems. However, it is clear that there is a lack of systematic literature review to identify and classify the FDD studies conducted within the scope of marine engineering. This paper offers a systematic review of FDD models particular to marine machinery and systems. The numbers of 72 core articles were highlighted through a comprehensive literature review conducted in the 2002–2022 period. The studies are classified based on the mostly utilized methods such as data-driven, model-based, knowledge-based, and new generation-hybrid. In addition, new generation and hybrid methods are discussed in detail. The experimental environment (i.e. shipboard, labs, simulator) and technical details of the conducted studies are extensively discussed. While 56.94% of the examined studies are related to the main engine, 43.06% of them are related to auxiliary engines. In addition, the main and auxiliary engine studies are also divided into subject headings and examined in detail. Given the recent developments in green and smart maritime concepts, a future research agenda of the FDD studies on marine machinery systems is then pinpointed. Consequently, the study stimulates scholars interested in FDD while it enables innovative ideas for marine engineers, technology providers, ship operators, and maritime entrepreneurs.

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Maritime educational program development responding to the industrial tendencies has recently become a challenging issue. In fact, International Convention on Standards of Training, Certification and Watchkeeping for Seafarers (STCW) sets competencies for maritime professionals in different ranks. However, it is so important to reconsider the relevant qualification standards and field expectations together. This paper illustrates a basic decision analysis particularly on STCW Code Table A-III/2 in order to identify the priorities of competencies in program development. In this case, Analytic Network Process (ANP) is adapted as a suitable technique to ensure dependencies and feedbacks between the competency items under different functions such as marine engineering at the management level, electrical, electronic and control engineering at the management level, maintenance and repair at the management level, etc. The initial results are useful to balance the methods for demonstrating competence (i.e. simulator training, laboratory equipment training, training ship experience in-service experience, etc.). As a further study, the proposed approach might be extended as a quality assurance tool strength the industrial compliance of program outcomes.

Identification of enabling technologies is a critical stage to manage digital transformation process in different industries. This paper investigates the priorities enabling technologies throughout digital transformation in ship management companies. As a suitable technique to this case, Analytic Network Process is utilized to clustering of organizational functions (i.e. technical management, operation management, etc.) and enabling technologies (i.e. big data analytics, internet of things, etc.). The results highlight big data analytics as the most important enabling technologies. Besides improving the managerial skills, ship management companies might consider the priorities to decide on the investments on digital transformation.

Shipboard Operation Human Reliability Analysis (SOHRA) method is recognized as a practical tool to predict human error probability (HEP) of operators engaging marine operations. Identifying the generic task types (GTTs) and marine specific error producing conditions (m-EPCs), the tool successfully derives HEP value distribution of critical operations in maritime environment. However, the real-time applications of SOHRA to prioritize and implement the suitable recovery actions have still open for development due to the limited time and expertise at pre-operation stage. This paper adapts SOHRA into lifeboat and davits inspection process as a critical marine safety service. Considering the tasks conducted by marine safety service engineers in routine and remote support modes, the GTTs and m-EPCs are assigned to estimate HEP values. In this context, a remote assistance system, is alternatively extended to involve standardization, camera tracking, and advisory support to enhance human reliability through inspection stages. The findings spotlight the deviation in HEP between routine ( 8.26E + 00) and remote support (4.04E-01) modes. A set of recovery actions (i.e. instructional materials) to remedy the HEP values in routine mode are suggested while it is not required at the remote mode assistance. The application illustrates that remote supporting to the marine service might reasonably reduce service engineers’ error rates. Consequently, the study is expected to enhance SOHRA applications in inspection period, particularly added value to marine service engineers in duty. The further studies on the proposed remote assistance concept as new generation solution will contribute to the service quality of marine safety companies.