Condition monitoring in an
on-ship environment
Mike Knowles and David Baglee
Institute for Automotive and Manufacturing
Advanced Practice (AMAP)
University of Sunderland
Who we are - AMAP
• AMAP is part of the Faculty of Applied Sciences
within the University of Sunderland
• AMAP has been involved in a number of projects
in:
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Low Carbon Vehicle Manufacturing
Digital Manufacturing
Reliability and Condition Monitoring(Posseidon)
Industrial Maintenance and Efficiency
Facilities and Projects
• Projects
– Dynamic Decisions in Maintenance (DYNAMITE)
– Intelligent Energy and Maintenance Management
– Digital Factory
• Digital Manufacturing
–
–
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CAD
CNC
Rapid Prototyping
Dynamometer
Driving Simulators
Posseidon Project - Background
• Progressive Oil Sensor System for
Extended Identification ON-Line
• Failures in marine diesel engines can be
costly and can cause extreme
inconvenience
• Current approaches to oil-based condition
monitoring involve samples being sent for
land based testing.
Impact of failures
• Engine failures can prove to be costly due
to delays, time to repair and, in certain
cases, environmental costs dues to ships
running aground
• Thus onboard Condition Monitoring was
borne out of need.
Posseidon
• The Posseidon projects seeks to address these
problems by providing a means to monitor the
condition of engine lubricating oil
Partners
•
Fundación Tekniker
•
BP Marine
•
OelCheck
•
Martechnic
•
IMM
•
Rina
•
IB Krates
•
University of Sunderland
Diesel Engine Fault Modes
Fault
Symptoms visible in oil properties
Corrosive Wear
High increase in wear metals,
A strongly decreased TBN compared to the fresh oil.
Abrasive (mechanical) wear
Incorrect viscosity.
Wear particles can be detected optically
Magnetic testing can reveal the presence of Iron.
Deposits
The TBN of the drip oil can become slightly decreased compared to the fresh oil
Additionally the calcium content of the drip oil is decreased compared to the fresh oil.
Adhesive (mechanical) wear
A strong loss of the viscosity compared to the fresh oil.
Magnetic testing can reveal the presence of large amounts of Iron
Severe sliding particles are visible optically
Soot Contamination
Detection of soot particles by IR methods
Increase in Viscosity
Oxidation
Increase in Viscosity
Mixture with another oil type
Change in Viscosity
Water Contamination
Detection of Water by IR methods
Nitration/Sulfation from Blow
by gases
Change in base number
Oil Analysis
• Oil analysis at land based laboratories makes advanced
analysis possible. Measurements taken include:
– Measurement of water content using Karl Fisher titration
– Measurement of TBN
– Particle counting using optical techniques to detect wear
particles
– Infrared spectroscopy techniques for measuring oil condition and
contaminants.
– Magnetic PQ index testing to measure iron particle content
– Density
– Viscosity
– Viscosity Index
– Fuel Content
– Flash Point
Sensor selection
Sensor
Output
IR sensor
Water concentration
Soot concentration
TBN
Viscosity sensor
Viscosity
FTIR sensor
TBN
Water content
Insoluble content
Optical particle detector
Particles
IR Sensor
• Developed by IMM
• Monitors water concentration, soot
concentration and TBN
Viscosity Sensor
• Developed by IMM
• Functions on vibrating pin principle
housing
(coils)
thread M30
thermocouple
Pin
Optical Particle Detector
• Developed by Tekniker
• The smallest particles which can be
identified are around 0.1 micron
Role of software
There are two levels of functionality for the system,
at the most basic level:
– Log the data
– Display the data
– Give simple assessments of oil condition and
potential faults
– Offer simple guidance messages to the operator.
While the more advanced requirements are:
– Exploit the multivariate nature of fault conditions
– Detect both immediate, fast developing faults and
longer-term, incipient fault
Technologies used
• Java
– Platform independence
• XML
– Data can be read by spreadsheets etc
– Configuration and condition monitoring limits
can easily be edited
Configuration – Design for
Extensibility
<config>
<datalogConfig>
<retrievalIntervalShort>0</retrievalIntervalShort>
<retrievalIntervalLong>3000</retrievalIntervalLong>
<xmlfile>\xmldata\sensorReadings.xml</xmlfile>
</datalogConfig>
<main>
<title>Posseidon Software Version 2</title>
<limitfile>\xmldata\CMLimits.xml</limitfile>
<messagefile>\xmldata\messages.xml</messagefile>
</main>
<BN>
<HKBFile>\BayesianNetwork\DieselEngine.hkb</HKBFile>
</BN>
<sensorConfig>
<sensor>
<name>Water</name>
<id>N</id>
<units>%</units>
</sensor>
<sensor>
<name>Visosity</name> <id>V</id>
</sensor>
</sensorConfig>
</config>
<units>cSt</units>
Bayesian Network
• An artificial intelligence module was developed
based on a Bayesian network to evaluate the
probabilities of various faults and component
failures
Screenshot
Testing
Posseidon Acheivements
• The need for the product has been
demonstrated
• The viability of the system has been
proved by the development of the
prototype system
Future Development
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Hardware and Miniaturisation
Display technologies
Extensibility and Sensor Selection
On-board/Off-board connectivity
Design Issues
Hardware and miniaturisation
• Progress has
already been made
on miniaturising the
individual sensors.
• Bespoke design is
now required to
produce a reliable
and robust unit
Display Technologies
• Robust display technologies exist which
support marine communication standards
and which offer the desired level of
robustness.
Extensibility
• Future Sensor additions – beyond oil
– Vibration
– Temperature
– Thermal Imaging
– Exhaust Emissions
Onboard/Offboard Connectivity
• Onboard
– NMEA 2000 – Supported by proposed display
units
– Inter-sensor connectivity – WSNs?
• Ground to shore connectivity
– Cost
– Update rate
Design issues
• What info is displayed?
– Use of software ‘mock-ups’ to obtain feedback
from engineering personnel
• Resilience
– Use of bespoke test rigs to simulate vibration,
thermal conditions etc.
Proposed Development Plan
• Create a consortium of interested parties
who can support development
• Produce refined prototype
– Smaller Sensors
– No Laptop
– Refined Software developed in collaboration
with industry
• Support needed:
– Direct input from Shipping operators
– Sensor/instrumentation companies.
Acknowledgements
• This work was supported by the EU
Framework Programme 6 under the
Posseidon project.
Thank you for listening
Questions?