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.

摘要

基于状态的维护是一种实现工业物联网来监控资产状况的维护策略。尽管有不可否认的好处，但也遇到了一些挑战，例如传感器数据的不完整。因此，虽然数据插补是一项重要的实践，但海运业缺乏对数据插补的分析和形式化。因此，通过实施一阶马尔可夫链与基于 16 个机器学习和时间序列预测模型的比较方法的多元插补方法相结合，引入了一种新的框架。为了突出其性能效率，通过对总共 4 个参数的案例研究的实施，在提议的框架和 MICE 方法之间进行了比较研究，从安装在货船海洋机械系统上的传感器获得。结果表明提高了 21-77%，表明其作为数据插补技术的性能效率。