Application of NARX neural network for predicting marine engine performance parameters

ABSTRACT 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 assisting decision makers to select appropriate maintenance actions for critical ship machinery. This paper develops a Nonlinear Autoregressive with Exogenous Input (NARX) ANN for forecasting future values of the exhaust gas outlet temperature of a marine main engine cylinder. A detailed sensitivity analysis is conducted to examine the performance and robustness of the NARX model for variations in the time series data, demonstrating virtuous performance and generalisation capabilities for forecasting and the ability to employ the model for monitoring and prognostic applications.

[1]  Zhigang Tian,et al.  A neural network approach for remaining useful life prediction utilizing both failure and suspension histories , 2010 .

[3]  Igor N. Aizenberg,et al.  Multilayer Neural Network with Multi-Valued Neurons in time series forecasting of oil production , 2014, Neurocomputing.

[4]  Junfeng Zhou,et al.  The fault diagnosis of marine engine cooling system based on artificial neural network (ANN) , 2010, 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE).

[5]  D. A. M.Sc Taylor.  Introduction to marine engineering , 1983 .

[6]  Iraklis Lazakis,et al.  Using artificial neural network-self-organising map for data clustering of marine engine condition monitoring applications , 2018 .

[7]  Hui Liu,et al.  Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks , 2015 .

[8]  Thomas Porathe,et al.  Autonomous Unmanned Merchant Vessel and its Contribution towards the e-Navigation Implementation: The MUNIN Perspective , 2014 .

[9]  Jianyuan Zhu.  Marine Diesel Engine Condition Monitoring by Use of BP Neural Network , .

[10]  Vasile Palade,et al.  Computational Intelligence in Fault Diagnosis , 2010 .

[11]  Jolanta Szoplik,et al.  Forecasting of natural gas consumption with artificial neural networks , 2015 .

[12]  Saltelli Andrea,et al.  Global Sensitivity Analysis: The Primer , 2008 .

[13]  Michael D. Murphy,et al.  Machine-learning algorithms for predicting on-farm direct water and electricity consumption on pasture based dairy farms , 2018, Comput. Electron. Agric..

[14]  Murat Kayri,et al.  Predictive Abilities of Bayesian Regularization and Levenberg–Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data , 2016 .

[15]  Zigor Uriondo,et al.  Condition-Based Maintenance for medium speed diesel engines used in vessels in operation , 2015 .

[16]  Howard B. Demuth,et al.  Neutral network toolbox for use with Matlab , 1995 .

[17]  Ricardo A. S. Fernandes,et al.  Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks , 2015, Appl. Soft Comput..

[18]  Mahmoud Nasr,et al.  Application of Artificial Neural Network (ANN) for the prediction of EL-AGAMY wastewater treatment plant performance-EGYPT , 2012 .

[19]  Rosemary Norman,et al.  Challenges and Opportunities of Big Data Analytics for Upcoming Regulations and Future Transformation of the Shipping Industry , 2017 .

[20]  Jawad Raza,et al.  Application of intelligent technique to identify hidden abnormalities in a system: A case study from oil export pumps from an offshore oil production facility , 2009 .

[21]  Davide Anguita,et al.  Condition-Based Maintenance of Naval Propulsion Systems with supervised Data Analysis , 2018 .

[22]  U. Epa,et al.  Guiding Principles for Monte Carlo Analysis , 1997 .

[23]  Subramaniam Ganesan,et al.  Condition based maintenance: a survey , 2012 .

[24]  Michael D. Shields,et al.  The generalization of Latin hypercube sampling , 2015, Reliab. Eng. Syst. Saf..

[25]  K. Hipel,et al.  Time series modelling of water resources and environmental systems , 1994 .

[26]  C. Chatfield.  Model uncertainty, data mining and statistical inference , 1995 .

[27]  Greg Sullivan,et al.  Operations & Maintenance Best Practices - A Guide to Achieving Operational Efficiency (Release 3) , 2010 .

[28]  Erol Egrioglu,et al.  Advances in Time Series Forecasting , 2012 .

[29]  Hayrettin Okut,et al.  Bayesian Regularized Neural Networks for Small n Big p Data , 2016 .

[30]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[31]  Massimo Caccia,et al.  MINOAS a Marine INspection rObotic Assistant: system requirements and design , 2010 .

[32]  Rizalman Mamat,et al.  Prediction of Marine Diesel Engine Performance by Using Artificial Neural Network Model , 2016 .

[33]  Iraklis Lazakis,et al.  Selection of the best maintenance approach in the maritime industry under fuzzy multiple attributive group decision-making environment , 2016 .

[34]  Diego Galar Pascual,et al.  Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and DIagnosis , 2015 .

[35]  P. Thompson,et al.  Guiding principles. , 2004, Hospitals & health networks.

[36]  Zhenzhou Lu,et al.  Monte Carlo simulation for moment-independent sensitivity analysis , 2013, Reliab. Eng. Syst. Saf..

[37]  Ray Pugh,et al.  Operations and Maintenance Best Practices--A Guide to Achieving Operational Efficiency , 2002 .

[38]  Chris Chatfield,et al.  Calculating Interval Forecasts , 1993 .

[39]  R. Asariotis,et al.  Review of Maritime Transport, 2014 , 2010 .

[40]  Pingfeng Wang,et al.  Failure diagnosis using deep belief learning based health state classification , 2013, Reliab. Eng. Syst. Saf..

[41]  Craig B. Borkowf,et al.  Time-Series Forecasting , 2002, Technometrics.

[42]  Lin Ma,et al.  Basis pursuit-based intelligent diagnosis of bearing faults , 2007 .

[43]  Zainal Ahmad,et al.  Optimum parameters for fault detection and diagnosis system of batch reaction using multiple neural networks , 2012 .