condition monitoring in an on-ship environment
Post on 12-Feb-2016
47 Views
Preview:
DESCRIPTION
TRANSCRIPT
Condition monitoring in an on-ship environment
Mike Knowles and David BagleeInstitute 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:– 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– 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 ModesFault 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 IronSevere sliding particles are visible optically
Soot Contamination Detection of soot particles by IR methodsIncrease 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 selectionSensor 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
thread M30
Pin
thermocouple
housing(coils)
Optical Particle Detector
• Developed by Tekniker• The smallest particles which can be
identified are around 0.1 micron
Role of softwareThere 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> <units>cSt</units></sensor>
</sensorConfig></config>
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
• 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?
top related