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Norsk Marinteknisk Forskningsinstitutt
Lokukaluge Prasad PereraNorwegian Marine Technology Research Institute (MARINTEK), Trondheim, Norway.
March 2016.
Trondheim, Norway.
Full Scale Data Handling in Shipping: A Big Data Solution.
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•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
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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.
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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"
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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
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Energy Flow Path
•The possible situations of energy conservation:− Marine power plant.
− Engine propeller interaction.
− Ship resistance.
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Marine Engine Centered Approach
Localized Models in Ship Performance Monitoring
Engine Propeller Combinator Diagram
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Engine Propeller Combinator Diagram
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Engine Propeller Combinator Diagram
Possible Region of Engine-Propeller Operations
Basis for Localized Models in Ship Performance Monitoring
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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
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Statistical distributions of Engine Speed, Power & Fuel Consumption
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Statistical distributions of Engine Speed, Power & Fuel Consumption
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Engine Operating Regions: Engine Power vs. Shaft Speed
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Engine Operating Region vs. Rel. Wind Speed, Avg. Draft, Trim and STW
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Engine Propeller Combinator Diagram
Operating Patterns
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Engine propeller combinator diagram with STW
High to Low STW
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Data Flow Path
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Data Flow Path
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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]
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Data Classification: Engine Propeller Combinator Diagram
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Localized Models
Principal
Component
Analysis
(PCA)
Model 3
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Sensor Fault Detection
Considering
a Two Sensor Situation
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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
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Fault Level 1
Considering a Two Sensor Situation
e3
e1
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Fault Level 2Principal Component Analysis (PCA)
Data Standardization.
Mean = 0
Variance = 1
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Least Principal Components
Projected Data into PCs
e1, e2, e3, & e4
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Data Distributions in PC Axes
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Least Principal Components
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Real-time FaultDetection
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Real-time FaultDetection
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Data Flow Path
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Autoencoder : Deep Learning Approach
•Encoder Side: Data Compression
•Communication Network
•Decoder Side: Data Expansion
•Top Principal Components
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Autoencoder : A Deep Learning Approach
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Autoencoder : A Deep Learning Approach
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Autoencoder : A Deep Learning Approach
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Autoencoder : A Deep Learning Approach
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Autoencoder : A Deep Learning Approach
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Autoencoder : A Deep Learning Approach
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Autoencoder : A Deep Learning Approach
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Singular Values Data 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.
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PCA of Ship Performance and Navigation Information.
PCs for Ship Performance Evaluation ?
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PCA of Ship Performance and Navigation Information.
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Actual and estimated parameters of ship performance and navigation information.
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Actual and estimated parameters of ship performance and navigation information.
Data can be Recovered by Regression or Smoothing
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Data Flow Path
Data can be Recovered by
Regression or Smoothing
AIS Data
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Data Analysis
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Relative Wind Profile of a Ship
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Relative Wind Profile with STW and SOG
Gaussian Type Distributions
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Wind Sensor Faults
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One Sided Relative Wind Profile (Cleaned Data)
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One Sided Relative Wind Profile with STW (> 3 Knots)
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One Sided Relative Wind Profile (Further Cleaned Data)
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Ship Speeds: STW, SOG and STW-SOG
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Ship Speed Power Profile with Relative Wind Speed|STW –SOG| < 5.5 (Knots)
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Speed Power Profile with Rel. Wind Speed and Angle
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STW-SOG vs. Rel. Wind Speed and Direction
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Modified Speed Power Profile
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SWT vs. SOGUnique to the Vessel ?
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Ship Performance Data
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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.
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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.