Various industrial challenges in full scale data handling situations in shipping are considered in this study. These large scale data handling approaches are often categorized as "Big Data" challenges; therefore various solutions to overcome such situations are identified. The proposed approach consists of a marine engine centered data flow path with various data handling layers to address the same challenges. These layers are categorized as: sensor fault detection, data classification, data compression, data transmission and receiver, data expansion, integrity verification, and data regression. The functionalities of each data handling layer with respect to ship performance and navigation information of a selected vessel are discussed and additional challenges that are encountered during this process are also summarized. Hence, these results can be used to develop data analytics that are related to energy efficiency and system reliability applications of shipping.

1. Norsk Marinteknisk Forskningsinstitutt
Lokukaluge Prasad Perera
Norwegian Marine Technology Research Institute (MARINTEK), Trondheim, Norway.
March 2016.
Trondheim, Norway.
Full Scale Data Handling
in Shipping: A Big Data Solution.

2. •Introduction
•Objectives
•Data Analytics & Sensors
•Energy Flow Path: Marine Engine Centered Approach
•Data Flow Path: Big Data Challenges
− Sensor Fault Identification
− Data Classification
− Data Compression
− Data Expansion
− Data Integrity
− Data Regression
•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 Big Data, 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 of the data
− Data communication
− Data Interpretation => Data Analytics
− Energy Efficiency
− Reliability
" The data has a structure and
the structure has a meaning"

5. Data Analytics & Sensors
•The main focus point
•Empirical/Stochastic Models
− Various Empirical/Stochastic Models have been developed in shipping.
− Some challenges in handling Big Data.
•Machine Intelligence
− Machine Intelligence (MI) can play an important role in the outcome of Big
Data applications.
− MI Techniques are extensively implemented on current Big Data
applications.
− These tools and techniques and their applicability in shipping should be
investigated.
•Knowledge on the Vessel:
− Ship Dynamics/Hydrodynamics
− Automation and Navigation Systems
− Localized Models in Ship Performance Monitoring

6. Energy Flow Path
•The possible situations of energy conservation:
− Marine power plant.
− Engine propeller interaction.
− Ship resistance.

7. Marine Engine Centered Approach
Localized Models in
Ship Performance Monitoring
Engine Propeller
Combinator Diagram

8. Engine Propeller Combinator Diagram

9. Engine Propeller Combinator Diagram
Possible Region of
Engine-Propeller
Operations
Basis for Localized
Models in Ship
Performance
Monitoring

10. Vessel Information
•The respective data set of ship performance and navigation
information is collected from:
•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 ME 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

11. Statistical distributions of
Engine Speed, Power & Fuel Consumption

12. Statistical distributions of
Engine Speed, Power & Fuel Consumption

13. Engine Operating Regions: Engine Power vs. Shaft Speed

14. Engine Operating Region vs.
Rel. Wind Speed, Avg. Draft, Trim and STW

15. Engine Propeller Combinator Diagram
Operating
Patterns

16. Engine propeller combinator diagram with STW
High to Low STW

17. Data Flow Path

18. Data Flow Path

19. Data Classification
•Engine Centered Data Flow Path
•Localized Models in Ship Performance Monitoring
•Algorithm
− Multivariate Gaussian distribution
− Gaussian Mixture Models (GMMs)
− Expectation Maximization (EM) algorithm
[Source: Matlab.com]

20. Data Classification: Engine Propeller Combinator Diagram

21. Localized Models
Principal
Component
Analysis
(PCA)
Model 3

22. Sensor Fault Detection
Considering
a Two Sensor Situation

23. Fault Level 1
Considering 10 Parameter Situation
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

24. Fault Level 1
Considering a Two Sensor Situation
e3
e1

25. Fault Level 2Principal Component Analysis (PCA)
Data
Standardization.
Mean = 0
Variance = 1

26. Least Principal Components
Projected Data into PCs
e1, e2, e3, & e4

27. Data Distributions
in PC Axes

28. Least Principal Components

29. Real-time Fault
Detection

30. Real-time Fault
Detection

31. Data Flow Path

32. Autoencoder : Deep Learning Approach
•Encoder Side: Data Compression
•Communication Network
•Decoder Side: Data Expansion
•Top Principal Components

33. Autoencoder : A Deep Learning Approach

34. Autoencoder : A Deep Learning Approach

35. Autoencoder : A Deep Learning Approach

36. Autoencoder : A Deep Learning Approach

37. Autoencoder : A Deep Learning Approach

38. Autoencoder : A Deep Learning Approach

39. Autoencoder : A Deep Learning Approach

40. Singular ValuesData Compression Information
•Top 10, 9, 8 and 7 principal
components can preserve 100 %,
99.92%, 99.48%, 97.86% 94.03% of the
actual ship performance and
navigation information.
•The respective 99% and 95% lines are
also presented in the same figure.
•Top 7 PCs Selected
•10 Parameters => 7 Parameters
•Preserve approximately 94% of the
actual information.

41. PCA of Ship Performance and Navigation Information.
PCs for Ship Performance Evaluation ?

42. PCA of Ship Performance and Navigation Information.

43. Actual and estimated parameters of ship performance
and navigation information.

44. Actual and estimated parameters of ship performance
and navigation information.
Data can be Recovered by Regression or Smoothing

45. Data Flow Path
Data can be Recovered by
Regression or Smoothing
AIS Data

46. Data Analysis

47. Relative Wind Profile of a Ship

48. Relative Wind Profile with STW and SOG
Gaussian Type Distributions

49. Wind Sensor Faults

50. One Sided Relative Wind Profile (Cleaned Data)

51. One Sided Relative Wind Profile with STW (> 3 Knots)

52. One Sided Relative Wind Profile (Further Cleaned Data)

53. Ship Speeds: STW, SOG and STW-SOG

54. Ship Speed Power Profile with Relative Wind Speed
|STW –SOG| < 5.5 (Knots)

55. Speed Power Profile with Rel. Wind Speed and Angle

56. STW-SOG vs. Rel. Wind Speed and Direction

57. Modified Speed Power Profile

58. SWT vs. SOG
Unique to the Vessel ?

59. Ship Performance Data

60. Conclusion & Future Activities
•Some advanced tools are developed in this stage.
•Still a Logway to go..
− Sensor Fault Identification
− Data Classification
− Data Compression
− Data Expansion
− Data Integrity
− Data Regression
•Models should be further developed.
•High sampling rate data
•Further collaboration with appropriate partners.
•Research projects.

61. 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.