A real-time data-driven framework for the identification of steady states of marine machinery. (April 2022)

• Record Type: Journal Article
• Authors: Velasco-Gallego, Christian; Lazakis, Iraklis
• Is Part Of: Applied ocean research. Volume 121(2022)
• Publication Date: 2022-04
• Subjects: Smart maintenance -- Marine machinery -- Maritime transportation -- Connected component analysis -- Steady states identification -- Markov chains; Ocean engineering -- Periodicals; 620.416205
• DOI: 10.1016/j.apor.2022.103052
• Languages: English
• ISSNs: 0141-1187

Abstract

Abstract: While maritime transportation is the primary means of long-haul transportation of goods to and from the EU, it continues to present a significant number of casualties and fatalities owing to damage to ship equipment; damage attributed to machinery failures during daily ship operations. Therefore, the implementation of state-of-the-art inspection and maintenance activities are of paramount importance to adequately ensure the proper functioning of systems. Accordingly, Internet of Ships paradigm has emerged to guarantee the interconnectivity of maritime objects. Such technology is still in its infancy, and thus several challenges need to be addressed. An example of which is data preparation, critical to ensure data quality while avoiding biased results in further analysis to enhance transportation operations. As part of developing a real-time intelligent system to assist with instant decision-making strategies that enhance ship and systems availability, operability, and profitability, a data-driven framework for the identification of steady states of marine machinery based on image generation and connected component analysis is proposed. The identification of such states is of preeminent importance, as non-operational states may adversely alter the results outlined. A case study of three diesel generators of a tanker ship is introduced to validate the developed framework. Results of this study demonstrated the outperformance of the proposed model in relation to the widely implemented clustering models k- means and GMMs with EM algorithm. As such, the proposed framework can adequately identify steady states appropriately to guarantee the detection of such states in real-time, whilst ensuring computational efficiency and model effectiveness.