A novel framework for imputing large gaps of missing values from time series sensor data of marine machinery systems. (3rd August 2022)

• Record Type: Journal Article
• Authors: Velasco-Gallego, Christian; Lazakis, Iraklis
• Is Part Of: Ships and offshore structures. Volume 17:Number 8(2022)
• Page Start: 1802
• Page End: 1811
• Publication Date: 2022-08-03
• Subjects: Data imputation -- marine machinery systems -- first-order Markov chain -- machine learning models -- condition-based maintenance (CBM) -- maritime transport; Ships -- Periodicals; Offshore structures -- Periodicals; Marine engineering -- Periodicals; Marine engineering -- Technological innovations -- Periodicals; Ocean engineering -- Periodicals; Ocean engineering -- Technological innovations -- Periodicals; 623.8
• DOI: 10.1080/17445302.2021.1943850
• Languages: English
• ISSNs: 1744-5302

Abstract

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.