Journal of Ocean Engineering and Science

Volume 5, Issue 4

(5)Developing a predictive maintenance model for vessel machinery

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Veronica Jaramillo Jimenez^a,∗, Noureddine Bouhmala^a, Anne Haugen Gausdal^b

^aDepartment of Maritime Operations, University of South-Eastern Norway

^bKristiania University College

Received 30 July 2019; received in revised form 11 March 2020; accepted 25 March 2020

Available online 15 May 2020

Abstract

The aim of maintenance is to reduce the number of failures in equipment and to avoid breakdowns that may lead to disruptions during operations. The objective of this study is to initiate the development of a predictive maintenance solution in the shipping industry based on a computational artificial intelligence model using real-time monitoring data. The data analysed originates from the historical values from sensors measuring the vessel´s engines and compressors health and the software used to analyse these data was R. The results demonstrated key parameters held a stronger influence in the overall state of the components and proved in most cases strong correlations amongst sensor data from the same equipment. The results also showed a great potential to serve as inputs for developing a predictive model, yet further elements including failure modes identification, detection of potential failures and asset criticality are some of the issues required to define prior designing the algorithms and a solution based on artificial intelligence. A systematic approach using big data and machine learning as techniques to create predictive maintenance strategies is already creating disruption within the shipping industry, and maritime organizations need to consider how to implement these new technologies into their business operations and to improve the speed and accuracy in their maintenance decision making.

This is an open access article under the CC BY-NC-ND license. (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Keywords: Maintenance in Shipping industry; Big Data Analytics; Vessel Machinery; Sensor Systems; Sensor Data; Condition Based Maintenance; Predictive Maintenance.