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Condition monitoring in an on-ship environment

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

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Condition monitoring in an on-ship environment

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  1. Condition monitoring in an on-ship environment Mike Knowles and David Baglee Institute for Automotive and Manufacturing Advanced Practice (AMAP) University of Sunderland

  2. 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

  3. 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

  4. 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.

  5. 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.

  6. Posseidon • The Posseidon projects seeks to address these problems by providing a means to monitor the condition of engine lubricating oil

  7. Partners • Fundación Tekniker • BP Marine • OelCheck • Martechnic • IMM • Rina • IB Krates • University of Sunderland

  8. Diesel Engine Fault Modes

  9. 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

  10. Sensor selection

  11. IR Sensor • Developed by IMM • Monitors water concentration, soot concentration and TBN

  12. Viscosity Sensor • Developed by IMM • Functions on vibrating pin principle

  13. Optical Particle Detector • Developed by Tekniker • The smallest particles which can be identified are around 0.1 micron

  14. 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

  15. Technologies used • Java • Platform independence • XML • Data can be read by spreadsheets etc • Configuration and condition monitoring limits can easily be edited

  16. 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>

  17. Bayesian Network • An artificial intelligence module was developed based on a Bayesian network to evaluate the probabilities of various faults and component failures

  18. Screenshot

  19. Testing

  20. 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

  21. Future Development • Hardware and Miniaturisation • Display technologies • Extensibility and Sensor Selection • On-board/Off-board connectivity • Design Issues

  22. 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

  23. Display Technologies • Robust display technologies exist which support marine communication standards and which offer the desired level of robustness.

  24. Extensibility • Future Sensor additions – beyond oil • Vibration • Temperature • Thermal Imaging • Exhaust Emissions

  25. Onboard/Offboard Connectivity • Onboard • NMEA 2000 – Supported by proposed display units • Inter-sensor connectivity – WSNs? • Ground to shore connectivity • Cost • Update rate

  26. 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.

  27. 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

  28. Support needed: • Direct input from Shipping operators • Sensor/instrumentation companies.

  29. Acknowledgements • This work was supported by the EU Framework Programme 6 under the Posseidon project.

  30. Thank you for listening Questions?

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