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<p>Ocean shipping is the backbone of international trade contributing to global economic growth. Consequently, ensuring that ships operate in an energy-efficient manner is crucial to a more sustainable global transportation. Engine failures in these contexts can lead to severe consequences including compromised safety, operational disruptions, and substantial economic losses ranging between 10% and 30% of total operating costs due to unscheduled maintenance. The proposed research integrates marine diesel engines diagnostics with machine learning (ML) algorithms to develop an advanced proactive maintenance strategy to anticipate engine performance trends and proactively identify potential faults before they escalate. Employing an experimental approach on a 4-stroke diesel engine, the controlled simulations were conducted to replicate various failure scenarios to collect data and capture crucial metrics such as temperatures across cylinders, vibrations along axes, and fluctuations in cooling water temperatures. The data were analysed using advanced ML algorithms aimed at enhancing the accuracy and reliability of future fault prediction, by employing a multivariate convolutional long short-term memory (ConvLSTM) model tailored for time series analysis, and a classification model using a random forest (RF) classifier. As a result, the ConvLSTM model decreased the RMSE by 15.4453% compared to decision tree regression models, while the RF classifier achieved an accuracy of 82.168%.</p>

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Proactive fault prediction in marine diesel engines using multivariate machine learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月20日

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  • Miral Michel,
  • Ahmed Mehanna,
  • Sherine Nagy Saleh,
  • Ahmed S. Shehata

摘要

Ocean shipping is the backbone of international trade contributing to global economic growth. Consequently, ensuring that ships operate in an energy-efficient manner is crucial to a more sustainable global transportation. Engine failures in these contexts can lead to severe consequences including compromised safety, operational disruptions, and substantial economic losses ranging between 10% and 30% of total operating costs due to unscheduled maintenance. The proposed research integrates marine diesel engines diagnostics with machine learning (ML) algorithms to develop an advanced proactive maintenance strategy to anticipate engine performance trends and proactively identify potential faults before they escalate. Employing an experimental approach on a 4-stroke diesel engine, the controlled simulations were conducted to replicate various failure scenarios to collect data and capture crucial metrics such as temperatures across cylinders, vibrations along axes, and fluctuations in cooling water temperatures. The data were analysed using advanced ML algorithms aimed at enhancing the accuracy and reliability of future fault prediction, by employing a multivariate convolutional long short-term memory (ConvLSTM) model tailored for time series analysis, and a classification model using a random forest (RF) classifier. As a result, the ConvLSTM model decreased the RMSE by 15.4453% compared to decision tree regression models, while the RF classifier achieved an accuracy of 82.168%.