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

OCEAN ENGINEERING

卷:218

Article

Velasco-Gallego, Christian¹  Lazakis, Iraklis¹ 

[1] Univ Strathclyde, Dept Naval Architecture Ocean & Marine Engn, 100 Montrose St, Glasgow G4 0LZ, Lanark, Scotland

关键词: Data imputation;    Machine learning;    Time series forecasting;    Marine machinery systems;    Condition-based maintenance (CBM);    Energy efficient operations;   

DOI  :  10.1016/j.oceaneng.2020.108261

来源: Elsevier

【 摘 要 】

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 preprocessing 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.

【 授权许可】

Free