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Research Papers: Offshore Technology

Marine Engine-Centered Data Analytics for Ship Performance Monitoring

[+] Author and Article Information
Lokukaluge P. Perera

Norwegian Marine Technology
Research Institute (MARINTEK),
Trondheim 7052, Norway
e-mail: Prasad.Perera@marintek.sintef.no

Brage Mo

Norwegian Marine Technology Research
Institute (MARINTEK),
Trondheim 7052, Norway
e-mail: Brage.Mo@marintek.sintef.no

Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received July 12, 2016; final manuscript received September 30, 2016; published online January 31, 2017. Assoc. Editor: Jonas W. Ringsberg.

J. Offshore Mech. Arct. Eng 139(2), 021301 (Jan 31, 2017) (8 pages) Paper No: OMAE-16-1081; doi: 10.1115/1.4034923 History: Received July 12, 2016; Revised September 30, 2016

This study proposes marine engine centered data analytics as a part of the ship energy efficiency management plan (SEEMP). The SEEMP enforces various emission control measures to improve ship energy efficiency by considering vessel performance and navigation data. The proposed data analytics is developed in the engine-propeller combinator diagram (i.e., one propeller shaft with a direct drive main engine). Three operating regions from the initial data analysis are under the combinator diagram noted to capture the shape of these regions by the proposed data analytics. The data analytics consists of implementing Gaussian mixture models (GMMs) to classify the most frequent operating regions of the main engine. Furthermore, the expectation maximization (EM) algorithm calculates the parameters of GMMs. This approach, also named data clustering algorithm, facilitates an iterative process for capturing the operating regions of the main engine (i.e., in the combinatory diagram) with the respective mean and covariance matrices. Hence, these data analytics can monitor ship performance and navigation conditions with respect to engine operating regions as a part of the SEEMP. Furthermore, development of advanced mathematical models for ship performance monitoring within the operational regions (i.e., data clusters) of marine engines is expected.

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References

IMO, 2009, “ Resolution MEPC.1/Circ.683, Guidelines for the Development of a Ship Energy Efficiency Management Plan (SEEMP),” International Maritime Organization, London.
IMO, 2012, “Resolution MEPC.213(63), 2012 Guidelines for the Development of a Ship Energy Efficiency Management Plan (SEEMP),” International Maritime Organization, London.
IMO, 2009, “ Resolution MEPC.1/Cric.684, Guidelines for the Voluntary Use of the Ship Energy Efficiency Operational Indicator (EEOI),” International Maritime Organization, London.
Perera, L. P. , and Mo, B. , 2016, “ Emission Control Based Energy Efficiency Measures in Ship Operations,” J. Appl. Ocean Res., 60, pp. 29–46. [CrossRef]
Perera, L. P. , and Mo, B. , 2016, “ Data Compression of Ship Performance and Navigation Information Under Deep Learning,” 35th International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2016), Busan, Korea, June 19–24, Paper No. OMAE2016-54093.
Perera, L. P. , Mo, B. , and Kristjansson, L. A. , 2015, “ Identification of Optimal Trim Configurations to Improve Energy Efficiency in Ships,” 10th IFAC Conference on Manoeuvring and Control of Marine Craft (MCMC 2015), Copenhagen, Denmark, Aug 24–26, 48(16), pp. 267–272.
MAN Diesel & Turbo, 2011, “ Basic Principles of Ship Propulsion,” MAN Diesel & Turbo, Copenhagen, Denmark, pp. 24–32.
Perera, L. P. , Rodrigues, J. M. , Pascoal, R. , and Guedes Soares, C. , 2012, “ Development of an Onboard Decision Support System for Ship Navigation Under Rough Weather Conditions,” Sustainable Maritime Transportation and Exploitation of Sea Resources, E. Rizzuto , and C. Guedes Soares , eds., Taylor & Francis Group, London, pp. 837–844.
Perera, L. P. , Machado, M. M. , Valland, A. , and Manguinho, D. A. P. , 2015, “ System Reliability of Offshore Gas Turbine Engines With Erroneous Data Conditions,” 25th European Safety and Reliability Conference (ESREL 2015), Zurich, Switzerland, Sept. 7–10, pp. 1679–1688.
Perera, L. P. , and Mo, B. , 2016, “ Ship Speed Power Performance Under Relative Wind Profiles,” Maritime Engineering and Technology III, Guedes Soares , and Santos , eds., Vol. 1, Taylor & Francis Group, London, pp. 133–141.
Perera, L. P. , and Mo, B. , 2016, “ Data Analytics for Capturing Marine Engine Operating Regions for Ship Performance Monitoring,” 35th International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2016), Busan, Korea, June 19–24, Paper No. OMAE2016-54168.
Sun, S. , Zhang, C. , and Yu, G. , 2006, “ A Bayesian Network Approach to Traffic Flow Forecasting,” IEEE Trans. Intell. Transp. Syst., 7(1), pp. 124–132. [CrossRef]
Moon, T. K. , 1996, “ The Expectation-Maximization Algorithm,” IEEE Signal Processing Magazine, 13(6), pp. 47–60. [CrossRef]
Ng, A. , 2016, Mixtures of Gaussians and the EM Algorithm, Lecture Notes on Machine Learning, Stanford University, Stanford, CA.
Perera, L. P. , and Mo, B. , 2016, “ Machine Intelligence for Energy Efficient Ships: A Big Data Solution,” Maritime Engineering and Technology III, Guedes Soares , and Santos , eds., Vol. 1, Taylor & Francis Group, London, pp. 143–150.

Figures

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Fig. 1

Simplified engine-propeller combinator diagram

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Fig. 2

Statistical distributions of marine engine parameters

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Fig. 3

Engine operation regions: ME power versus shaft speed

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Fig. 4

Engine operation region versus relative wind speed, STW, average draft, and trim

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Fig. 5

Engine propeller combinator diagram

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Fig. 6

Engine propeller combinator diagram with STW

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Fig. 7

GMMs in the engine propeller combinator diagram

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