Application of NARX neural network for predicting marine engine performance parameters

Raptodimos, Yiannis and Lazakis, Iraklis (2019) Application of NARX neural network for predicting marine engine performance parameters. Ships and Offshore Structures. ISSN 1754-212X (In Press)

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    Though the maritime industry is still predominantly reliant on a time-based, prescriptive approach to maintenance, the increasing complexity of shipboard systems, heightened expectation and competitive requirements as to ship availability and efficiency and the influence of the data revolution on vessel operations, favour a properly structured Condition Based Maintenance (CBM) regime. In this respect Artificial Neural Networks (ANNs) can be applied for predictive maintenance strategies that can assist decision makers in selecting appropriate maintenance actions for critical ship machinery. This paper focuses on developing a Nonlinear Autoregressive with Exogenous Input (NARX) ANN model for forecasting future values of performance parameters of a marine main engine. Moreover, a detailed sensitivity analysis is conducted to examine the performance and robustness of the developed NARX model, based on the dataset applied for its training. Through the NARX model, a predictive monitoring approach can be achieved for ship machinery monitoring as high forecasting accuracy was achieved for the case study of a main engine cylinder exhaust gas outlet temperature. The sensitivity analysis overall demonstrated the good performance and generalisation of the NARX model as it successfully considers different values of the time series data for conducting the one-step-ahead output.