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

Abstract

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.

Marine Systems and Equipment Prognostics and Health Management: A Systematic Review from Health Condition Monitoring to Maintenance Strategy

“…Other imputation methods were applied to wind turbines [22] and cluster monitoring [23]. The effects of different missing data imputation methods were compared using cargo ships' sensor data [24];…”

Estimation of Missing LiDAR Data for Accurate AGV Localization

“…The missing data is classified depending on the mechanism that caused it into four categories [8,9]: missing completely at random (MCAR), missing at random (MAR), a non-ignorable case or missing not at random (MNAR) and missing by natural design (MBND). Classical statistical methods include expectation-maximization (EM) [10][11][12], maximum likelihood, partial deletion, hot/cold deck, mean substitution [10,[13][14][15][16], etc., while more classical machine learning approaches include Markov Chain Monte Carlo computations [17,18], linear regression [13,14,19], KNN [10,11,[13][14][15][16]20], Support Vector Machines (SVMs) [13,14], Neural Networks (NNs) [10,13,21], Vector Autoregressions (VARs) [13], Decision Tree Regressors (DTRs) [13], or deep neural networks [9,16].…”

Data Preparation in Machine Learning for Condition-based Maintenance

“…Coraddu et al (2016;Cipollini et al, 2018), the authors chose the same metric on the same GT dataset example. Also, this metric has been successfully adopted in similar studies in recent years (Wisyaldin et al, 2020;Velasco-Gallego and Lazakis, 2020). Given that there are two outputs, the "GT compressor decay coefficient" and the "GT decay coefficient", this multi-target problem is tackled by decomposing it into two single target sub-problems.…”