An integrated machine learning framework for enhanced vessel operational efficiency

  • Christos Gkerekos

Student thesis: Doctoral Thesis


Inadequate machinery maintenance and inefficient sailing performance comprise two major hindrances to vessel operational sustainability and profitability. To ensure that vessel operation remains competitive while its environmental impact is mitigated, the development of a systematic approach for vessel monitoring and operational enhancement is required. Currently, the maritime industry predominantly operates on a hybridisation of corrective and preventive maintenance, along with monitoring and decision making based on past experience. More intelligent, data-driven approaches are slowly permeating the industry; these offerings however remain largely rudimentary, retaining considerable assumptions and data requirements for their application. In this respect, this thesis aims to enhance operational efficiency in the maritime industry through the development of an integrated machine learning framework combining efficient and robust machinery anomaly detection, vessel performance degradation monitoring, and routing decision support. This is achieved through a number of key objectives, including: a) the identification of research gaps; b) the extraction of meaningful information for available data sources; c) the monitoring of machinery condition and detection of incipient anomalies; d) the identification of optimal data-driven Fuel Oil Consumption (FOC) modelling architectures; e) the monitoring of vessel performance based on FOC modelling; the facilitation of optimal routing through a suitable Decision Support System (DSS); and f) the demonstration and validation of the above through appropriate case studies. The proposed aim and objectives are accomplished through the combination of a robust pre-processing methodology with a number of data-driven modelling methods (e.g. One-Class Support Vector Classifiers (OCSVCs), Deep Neural Networks (DNNs)), and a novel modification of Dijkstra’s algorithm. A key novelty aspect of this proposed framework is derived by the development and combination of a number of data-driven methodologies for the operational efficiency enhancement of a vessel. Moreover, a novelty of the approach lies upon the minimisation of the inherent assumptions required, streamlining its use in a diverse set of applications. In the same vein, a novel aspect of the proposed framework concerns its flexibility to operate using datasets from different sources, exhibiting different levels of granularity and frequency. This framework is applied to a number of case studies, covering data pre-processing, engine condition monitoring, a FOC modelling comparison, FOC-based performance monitoring, and optimal routing. This helps verify the framework’s robustness in a range of realistic scenarios applicable to a variety of vessel types (e.g. reefer, containership, bulk carrier). These case studies, among others, demonstrated the robustness of the anomaly detection methodology when examining different parameters and systems, the accuracy deviation when predicting a vessel’s FOC using Automated Data Logging & Monitoring (ADLM) or noon-report data and the optimal models for each case, a successful evaluation of the performance monitoring methodology as a vessel’s fouling increases; and the identification of optimal routes as a vessel sails from the Gulf of Guinea to Marseille anchorage.
Date of Award9 Oct 2020
Original languageEnglish
Awarding Institution
  • University Of Strathclyde
SponsorsUniversity of Strathclyde
SupervisorIraklis Lazakis (Supervisor) & Gerasimos Theotokatos (Supervisor)

Cite this