The document discusses handling big data in ship performance and navigation monitoring. It presents a data handling framework that includes developing digital models from data clusters, using principal component analysis to analyze the clusters, and extracting information to reduce parameters while preserving information. The framework allows for data projection, sensor fault detection, integrity verification from other sources, and data visualization to support decision making. The talk outlines developing these techniques to better handle large scale data from ships.

1. Lokukaluge Prasad Perera
SINTEF Ocean, Trondheim, Norway.
The Smart Ship Technology conference
The Royal Institution of Naval Architects,
UK January 2017, London, UK.
Handling Big Data in
Ship Performance &
Navigation Monitoring.

2. •Introduction
•Objectives
•Data Analytics & Internet of Things
•Data Handling Framework & Big Data Challenges
•Industrial Digitalization
•Conclusion & Future Activities
Outline
Data Analytics
Data
Management

3. Introduction
•Big Data Solutions play an important role in Future Research and
Industrial Applications.
•Strategic Priority Area for the MARINTEK.
•Research and Industrial Applications:
− Data Management: Appropriate actions to develop a bunch of data in a
structured collection.
− Data Analytics: The science of examining these data with the purpose of
drawing meanings about the information.
•The size of these data sets may not make a big difference in these
applications.
•The outcome of the Data set, the meaning, is the most important
aspect of these research and industrial applications.
•Many Fundamental Challenges.

4. Objectives
•To address the Fundamental Challenges in Big Data Applications
in Shipping.
− Large Scale Data Sources Data Management
− Sensor Related Issues
− Quality/Quantity of the data
− Data Communication
− Data Interpretation Data Analytics
− Energy Efficiency
− System Reliability
" The data has a structure and
the structure has a meaning"
A Journey towards a Meaningful Data Structure…
Social Analytics

5. Data Analytics & Internet of Things
•Conventional Models
− Various Conventional Models have been developed in shipping.
− Some challenges in handling Big Data : data modelling uncertainty, erroneous data
conditions, data visualization challenges and high computational power.
•Machine Intelligence & Statistical Analysis
− Machine Intelligence (MI) will play an important role in the outcome of Big
Data applications.
− Statistical Techniques will guide MI Applications.
− Such tools and techniques and their applicability as Data Driven Models.
•Domain Knowledge
− Ship Dynamics/Hydrodynamics
− Automation and Navigation Systems
− Engine Propeller Combinator Diagram

6. Data Handling Framework

7. Digital Models/Data Driven Models

8. Eigenvalues & Eigenvectors
Principal Component Analysis (PCA)

9. Information Extraction
•Data Driven Approach
− Self learning
− Self cleaning
− Self compression-expansion
− Multi-purpose structure
− Efficiency & Reliability

10. Engine Centered Approach
Digital Models
Engine Propeller
Combinator Diagram

11. Engine Propeller Combinator Diagram
Possible Region of
Engine-Propeller
Operations
Basis for
Digital Models

12. Vessel Information
•A set ship performance and navigation parameters is collected from
a selected vessel.
•Bulk Carrier with following particulars:
− ship length: 225 (m),
− beam: 32.29 (m),
− Gross tonnage: 38.889 (tons),
− deadweight at max draft: 72.562 (tons).
− Powered by 2 stroke Main Engine with maximum continuous rating
(MCR) of 7564 (kW) at the shaft rotational speed of 105 (rpm).
− Fixed pitch propeller diameter 6.20 (m) with 4 blades

13. Ship Performance and Navigation Parameters
Considering a 10 Parameter Data Set
Parameter Mini. Max.
1. Avg. draft (m) 0 15
2. STW (Knots) 3 20
3. ME power (kW) 1000 8000
4. Shaft speed (rpm) 20 120
5. ME fuel cons. (Tons/day) 1 40
6. SOG (Knots) 0 20
7. Trim (m) -2 6
8. Rel. wind speed (m/s) 0 25
9. Rel. wind direction (deg) 2 360
10. Aux. fuel cons. (Tons/day) 0 8

14. Histograms of Engine Parameters

15. Engine Propeller Combinator Diagram

16. Digital Models
Data Cluster 1
Data Cluster 2
Data Cluster 3

17. Ship Performance & Navigation Parameters

18. Digital Model

19. Localized Models
Principal
Component
Analysis
(PCA)
Data Cluster 3
Data Cluster 1
Data Cluster 2

20. Information Extraction

21. PCs on
Data Cluster 3

22. PCA on Model 3

23. Digital Models
Data Cluster 1
Data Cluster 2
Data Cluster 3

24. Data Projection
into PC Axes

25. Sensor & DAQ Fault Detection

26. Parameter Selection
•Top 7 PCs Selected.
•10 Parameters => 7
Parameters.
•Preserve
approximately 99.5%
of the actual
information.

27. Parameter Reduction/Error Compression
& Expansion/Data Recovery
Data can be Recovered
by Regression or Smoothing
Data Regression

28. Integrity Verification
Other Data Sources
Actual Weather Data

29. Data Visualization
Relative Wind Profile of a Ship

30. Data Visualization
Ship Speeds

31. Industrial Digitalization

32. •Some advanced tools & Techniques are developed in this stage.
•Still a logway to go..
− Digital Models
− Sensor & DAQ Fault Identification
− Parameter Reduction/Error compression
− Parameter Expansion/Data Recovery Data Structure
− Integrity Verification
− Data Regression
− Data Visualization
− Decision Supporting
•High sampling rate data.
•Research projects/topics.
Conclusion & Future Activities

33. Thank You
Questions ?
This work has been conducted under the project of "SFI Smart Maritime - Norwegian Centre for
improved energy-efficiency and reduced emissions from the maritime sector" that is partly
funded by the Research Council of Norway.
smartmaritime.no
Publications and high resolution color images: http://bit.do/perera.

34. Data Classification: Engine Propeller Combinator Diagram