Real-time data-driven missing data imputation for short-term sensor data of marine systems. A comparative study

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. •The importance of data imputation as a pre-processing step to avoid under-utilisation of data, and thus avoid leading to biased results.•The development of a methodological comparative study that examines a total of 20 implemented machine learning and time series imputation techniques.•The utilisation of a case study on a total of 7 ship machinery system parameters to highlight the implementation of the proposed methodology.•The suggestion of utilising VAR models when the implementation of a multivariate imputation technique is considered and ARIMA models otherwise.