A novel framework for imputing large gaps of missing values from time series sensor data of marine machinery systems

Abstract

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.

Health assessment of marine gas turbine propulsion system under cross-working conditions based on transfer learning

“…Cheliotis realized early fault detection of a marine main engine by using the Exponentially Weighted Moving Average with the data of cylinder exhaust gas temperature and scavenging air pressure [11]. Velasco proposed a framework based on the first-order Markov chain tandem with a multivariate imputation approach for data imputation in the maritime industry [12].…”