Transcript Condition monitoring in an on-ship environment - SURE
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: – 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
• Fundación Tekniker • BP Marine • OelCheck • Martechnic • IMM • Rina • IB Krates • University of Sunderland
Partners
Diesel Engine Fault Modes
Symptoms visible in oil properties Fault
Corrosive Wear Abrasive (mechanical) wear Deposits Adhesive (mechanical) wear Soot Contamination Oxidation Mixture with another oil type Water Contamination Nitration/Sulfation from Blow by gases High increase in wear metals, A strongly decreased TBN compared to the fresh oil. Incorrect viscosity. Wear particles can be detected optically Magnetic testing can reveal the presence of Iron.
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.
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 Detection of soot particles by IR methods Increase in Viscosity Increase in Viscosity Change in Viscosity Detection of Water by IR methods 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
IR sensor Viscosity sensor FTIR sensor
Sensor selection
Optical particle detector
Output
Water concentration Soot concentration TBN Viscosity TBN Water content Insoluble content 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
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?