Presentation on theme: "Condition monitoring in an on-ship environment Mike Knowles and David Baglee Institute for Automotive and Manufacturing Advanced Practice (AMAP) University."— Presentation transcript:
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
Partners Fundación Tekniker BP Marine OelCheck Martechnic IMM Rina IB Krates University of Sunderland
Diesel Engine Fault Modes FaultSymptoms visible in oil properties Corrosive WearHigh increase in wear metals, A strongly decreased TBN compared to the fresh oil. Abrasive (mechanical) wearIncorrect viscosity. Wear particles can be detected optically Magnetic testing can reveal the presence of Iron. DepositsThe 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) wearA 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 ContaminationDetection of soot particles by IR methods Increase in Viscosity OxidationIncrease in Viscosity Mixture with another oil typeChange in Viscosity Water ContaminationDetection 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
IR Sensor Developed by IMM Monitors water concentration, soot concentration and TBN
Viscosity Sensor Developed by IMM Functions on vibrating pin principle
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 0 3000 \xmldata\sensorReadings.xml Posseidon Software Version 2 \xmldata\CMLimits.xml \xmldata\messages.xml \BayesianNetwork\DieselEngine.hkb Water N % Visosity V cSt
Bayesian Network An artificial intelligence module was developed based on a Bayesian network to evaluate the probabilities of various faults and component failures
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.
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