handling big data in ship performance & navigation monitoring

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.

•Introduction

•Objectives

•Data Analytics & Internet of Things

•Data Handling Framework & Big Data Challenges

•Industrial Digitalization

•Conclusion & Future Activities

Outline

Data Analytics

Data

Management

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.

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

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

Data Handling Framework

Digital Models/Data Driven Models

Eigenvalues & Eigenvectors

Principal Component Analysis (PCA)

Information Extraction

•Data Driven Approach− Self learning

− Self cleaning

− Self compression-expansion

− Multi-purpose structure

− Efficiency & Reliability

Engine Centered Approach

Digital Models

Engine Propeller

Combinator Diagram

Engine Propeller Combinator Diagram

Possible Region of

Engine-Propeller

Operations

Basis for

Digital Models

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

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

Histograms of Engine Parameters

Engine Propeller Combinator Diagram

Digital Models

Data Cluster 1Data Cluster 2

Data Cluster 3

Ship Performance & Navigation Parameters

Digital Model

Localized Models

Principal

Component

Analysis

(PCA)

Data Cluster 3

Data Cluster 1

Data Cluster 2

Information Extraction

PCs on

Data Cluster 3

PCA on Model 3

Digital Models

Data Cluster 1Data Cluster 2

Data Cluster 3

Data Projection

into PC Axes

Sensor & DAQ Fault Detection

Parameter Selection

•Top 7 PCs Selected.

•10 Parameters => 7

Parameters.

•Preserve

approximately 99.5%

of the actual

information.

Parameter Reduction/Error Compression& Expansion/Data Recovery

Data can be Recovered

by Regression or Smoothing

Data Regression

Integrity Verification

Other Data Sources

Actual Weather Data

Data VisualizationRelative Wind Profile of a Ship

Data VisualizationShip Speeds

Industrial Digitalization

•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

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.

Data Classification: Engine Propeller Combinator Diagram