In the maritime industry, sensors are utilised to implement condition-based maintenance (CBM) to assist decision-making processes for energy efficient operations of marine machinery. However, the employment of sensors presents several challenges including the imputation of missing values. Data imputation is a crucial pre-processing step, the aim of which is the estimation of identified missing values to avoid under-utilisation of data that can lead to biased results. Although various studies have been developed on this topic, none of the studies so far have considered the option of imputing incomplete values in real-time to assist instant data-driven decision-making strategies. Hence, a methodological comparative study has been developed that examines a total of 20 widely implemented machine learning and time series forecasting algorithms. Moreover, a case study on a total of 7 machinery system parameters obtained from sensors installed on a cargo vessel is utilised to highlight the implementation of the proposed methodology. To assess the models' performance seven metrics are estimated (Execution time, MSE, MSLE, RMSE, MAPE, MedAE, Max Error). In all cases, ARIMA outperforms the remaining models, yielding a MedAE of 0.08 r/min and a Max Error of 2.4 r/min regarding the main engine rotational speed parameter.

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To enable Condition-Based maintenance, sensors need to be installed, and thus Internet of Ships (IoS) needs to be implemented. IoS presents several challenges, an example of which is the imputation of missing values. A data assessment imputation framework that is utilised to assess the accuracy of any imputation model is presented. To complement this study, a real-time imputation tool is proposed based on an open-source stack. A case study on a total of 4 machinery systems parameters obtained from sensors installed on a cargo vessel is presented to highlight the implementation of this framework. The multivariate imputation technique is performed by applying Kernel Ridge Regression (KRR). As the explanatory variables may also contain missing values, GA-ARIMA is utilised as the univariate imputation technique. The case study results demonstrate the applicability of the suggested framework in the case of marine machinery systems.

Condition-based maintenance is a maintenance strategy that implements Industrial Internet of Things to monitor the assets' condition. Despite its undeniable benefits, several challenges are encountered, such as the incompleteness of sensor data. Hence, while data imputation is an important practise, there is a lack of analysis and formalisation of data imputation in the maritime industry. Accordingly, a novel framework is introduced by implementing the first-order Markov chain in tandem with a multivariate imputation approach based on a comparative methodology of 16 machine learning and time series forecasting models. To highlight its performance efficiency, a comparative study is conducted between the proposed framework and the MICE approach by the implementation of a case study on a total of 4 parameters, obtained from sensors installed on the marine machinery systems of a cargo vessel. The results demonstrated an improvement of 21-77%, indicating its performance efficiency as a data imputation technique.