Developing a predictive maintenance model for vessel machinery

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 vessels 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. (c) 2020 Shanghai Jiaotong University. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license. (http://creativecommons.org/licenses/by-nc-nd/4.0/)