Machinery Lubrication magazine published by Noria Corporation
Issue link: https://www.e-digitaleditions.com/i/1433576
14 | November - December 2021 | www . machinerylubrication.com A large amount of data is needed to char- acterize the complete system; this data is not normally reported in sufficient detail in research papers for machine learning to be applied. In addition, the composition of lubri- cants is proprietary and usually only known to the lubricant and additive suppliers. Researchers, such as Professor Daniele Dini at Imperial College, are actively attempting to develop a "digital twin" of a tribological contact, but this is likely to be a long-term project which requires advances in a number of key areas (wear modeling, lubri- cant tribochemistry, material changes during sliding, etc.). In addition, any successful machine learning model will need to distin- guish between the "good wear" that occurs during "running-in" from the "bad wear" that occurs near the end of a component's life. Looking further to the future, researchers at the University of Central Lancashire, led by Professor Ian Sherrington, are actively working in the field of tribotronics, in which individual tribological systems are monitored, and contact conditions can be changed depending on the sensor data. For example, in two-stroke marine engines, the oil film thickness between the piston ring and liner can be measured using a capacitive sensor, and the oil feed rate to the cylinder can be increased or decreased depending on the oil film thickness. Such systems could benefit from machine learning algorithms too. Artificial intelligence and machine learning algorithms have already been used for many applications in lubrication and tribology. To progress further, the lubricant and tribological community will need to develop ways to share the large amounts of data that such models need. To date, most tribology tests generate relatively small amounts of data (compared, for example, to the number of photographs or text online, used for facial recognition and language translation); key material, surface roughness, and lubricant properties are not generally available for later analysis. In the near future, when machines are connected and routinely sending infor- mation back to their manufacturers (and customers) about how they are performing, the use of AI and machine learning to give early warning of potential issues or faults with the machine will enable proactive main- tenance to be undertaken, and customers will avoid potentially costly breakdowns und unscheduled machine downtime. ML Further reading: 1. Condition monitoring: see for example, https:// www.ge.com/digital/iiot-platform, https://www. skf.com/uk/products/condition-monitoring-sys- tems, https://w w w.boschrexroth.com/en/xc/ company/press/index2-31872, https://w w w. tribosonics.com/, https://www.machinerylubri- c a t i o n . c o m / R e a d / 3 1 9 8 2 / ue-systems-ontrak-smartlube 2. Shell Lubechat: https://w w w.shell.com/busi- n e s s - c u s t o m e r s / l u b r i c a n t s - f o r- b u s i n e s s / shell-expertise/lubrication-services-for-your-in- dustry/shell-lubechat.html 3. Fleet telematics: https://www.microlise.com/ 4. Classification of wear particles: https://www. s c ie nc e d i re c t .c om /s c ie nc e /a r t ic le /a b s /pi i / S0888327015004732 5. Design of new materials & lubricant properties: https://www.mdpi.com/2075-4442/9/1/2/pdf 6. Lubr ic at ion re g i me: ht t ps://w w w.mdpi. com/2075-4442/6/4/108 7. Predicting friction and wear: https://my.demio. com/recording/X4TOkK8i 8. Tribotronics: https://w w w.sciencedirect.com/ science/article/abs/pii/S0301679X07000631 About the Author: After a degree in Physics and a Ph.D. in Applied Physics and Electronics, Ian Taylor joined Shell Research in the UK in 1991, where he worked mainly in lubricant/ lubrication research (including being the Global Technology Manager for Shell's Lubrication Science team from 2006 to 2012). Most of his research focused on ener- gy-efficient lubricants and lubricant sensors/ condition monitoring. He was also respon- sible for managing Shell's University research links in tribology and lubrication (with Universities such as MIT, Tsinghua Univer- sity, Imperial College, Leeds University, etc.) from the mid-1990's to 2020. Ian is regularly invited to be a speaker at international tribology conferences and has published over 80 peer-reviewed papers in tribology. Ian left Shell in late 2020 and is now a Visiting Professor at the University of Central Lancashire. Ian is a Fellow of the UK's Insti- tute of Physics and a Fellow of the STLE. COVER STORY