34 Analytical Instrumentation


In a separate study, the same researchers conducted a comparative analysis of six different soft computing models for predicting the viscosity of diesel engine lubricants22


. Viscosity plays a


crucial role, especially at high temperatures, where a reduction in viscosity can lead to increased wear on mechanical parts, while an increase often signals oil oxidation and the accumulation of varnish or sludge23


at -40°C that is reduced to less than 10 mm2


. As shown in Figure 7, an ISO VG46 oil has a viscosity of 100,000 mm2 /s at 140°C24


/s . If the viscosity of the lubricant drops


too low, high operating temperatures would further decrease it, compromising the lubricant’s effectiveness. The models were trained using a dataset of 555 engine oil analysis reports, which included data on two oil types, metallic and non-metallic elements (indicative of contaminants/ additives), and engine operating hours. They found that the radial basis function (RBF) model consistently outperformed the others in terms of accuracy and consistency. Figure 8 shows that as network topology increased, the RBF model displayed a decrease in root mean square error (RMSE) and an increase in effi ciency (EF)22


. At the max network topology of 35 neurons, the RBF


model had an RMSE of 0.20 and an EF of 0.99 during training, and an RMSE of 0.11 and EF of 1 during testing.


References


1 Lansdown, A. R. (1982). Lubrication: A Practical Guide to Lubricant Selection. http://ci.nii.ac.jp/ ncid/BA07131179


2 Lubrication increases the effi ciency and life-expectancy of machines. (n.d.). https://www.graco. com/gb/en/vehicle-service/solutions/articles/what-is-lubrication-and-why-is-it-important. html


3 Admin. (2023, December 23). The Six Forms of Lubricant Degradation. Strategic Reliability Solutions Ltd. https://strategicreliabilitysolutions.com/the-six-forms-of-lubricant-degradation/


4 Radulescu, I., & Radulescu, A. V. (2020). Lubricants condition monitoring by using a global performance passport. IOP Conference Series Materials Science and Engineering, 724(1), 012035. https://doi.org/10.1088/1757-899x/724/1/012035


5 Safety Data Sheets. (2022, October 25). Safety Services. https://safetyservices.ucdavis. edu/units/ehs/research/safety-data-sheets#:~:text=The%20purpose%20of%20a%20 Safety,regarding%20chemical%20hazards%20and%20handling.


6 Detailed overview - Overview - About Us. (n.d.). https://www.astm.org/about/overview/detailed- overview.html


7 Halme, J., Gorritxategi, E., Bellew, J. (2010). Lubricating Oil Sensors. In: Holmberg, K., Adgar, A., Arnaiz, A., Jantunen, E., Mascolo, J., Mekid, S. (eds) E-maintenance. Springer, London. https:// doi.org/10.1007/978-1-84996-205-6_7


8 Axlebox Bearings for Passenger Cars & Locomotives. (n.d.). Schaeffl er Group USA Inc. https:// www.schaeffl er.us/us/products-and-solutions/industrial/industry_solutions/rail/axlebox_ bearings_passenger_cars_locomotives/


9 The evolution of railway axlebox technology - Evolution. (2020, January 17). Evolution - Technology Magazine From SKF. https://evolution.skf.com/the-evolution-of-railway-axlebox- technology/


Figure 5: Relationship between viscosity of an ISO VG46 oil and temperature24


10 Arapen RB 320. (n.d.). https://www.mobil.com/en/lubricants/for-businesses/industrial/ lubricants/products/products/arapen-rb-320


11 Palub. (2020, September 1). Understanding the viscosity index of a lubricant. Q8Oils. https:// www.q8oils.com/energy/viscosity-index/#:~:text=The%20viscosity%20index%20of%20a%20 lubricant%20is%20determined%20by%20measuring,can%20be%20up%20to%20120.


12 Dubek, K., Schneidhofer, C., Dörr, N., & Schmid, U. (2024). Laboratory robustness validation of a humidity sensor system for the condition monitoring of grease-lubricated components


About the Authors


Figure 6: Impact of network topology on RBF model performance in terms of RMSE and EF during training (left) and testing (right)22


By combining soft computing with historical datasets, the software can learn patterns and relationships between parameters, allowing it to identify potential issues early on and send out notifi cations for preventative maintenance or replacement. While these results paint an optimistic future for AI in LCM, the models still depend heavily on the diversity of situations in their training dataset. Over-reliance on these models could potentially lead to careless mistakes and expose vulnerabilities in AI when faced with unforeseen errors.


6. Conclusion


Real-time LCM through a combination of robust sensors and reliable software will be key to maintaining equipment health and optimizing performance with minimal system disturbances. An essential step towards achieving this is making information about lubricants, particularly those in common use, easily accessible, such as through a global performance passport or shared online databases. While sensors are already commercially available, specifi c testing must be conducted based on the industry and machinery in which they will be used. Developing a dedicated sensor for every application is impractical and time-consuming, so the ideal approach would be to repurpose existing technologies for new applications. In addition, vibration analysis has to be further evaluated for relevant correlations with lubricant health through studying different machinery across industries.


Although there is ongoing debate about the reliability of AI in LCM, the focus should be on creating software that is fl exible enough to adapt to changes yet detailed enough to detect subtle shifts that may indicate current or future lubricant issues. Studies pertaining to the implementation of LCM in current machinery suggest that the process will be non-invasive, eliminating the need for entirely new equipment. Replacing a machine is always more expensive than replacing its lubricant, so incorporating additional systems – though potentially costly upfront – would be more cost-effective than dealing with frequent machine breakdowns caused by missing the critical moments for lubricant replacement.


Even without fully relying on AI programs, it remains an important factor in making LCM incorporation into different industries seamless, a process that should ultimately be guided and overseen by human expertise.


Dr. Raj Shah is a Director at Koehler Instrument Company in New York, where he has worked for the last 25 plus years. He is an elected Fellow by his peers at IChemE, AOCS, CMI, STLE, AIC, NLGI, INSTMC, Institute of Physics, The Energy Institute and The Royal Society of Chemistry. An ASTM Eagle award recipient, Dr. Shah recently coedited the bestseller, “Fuels and Lubricants handbook”, details of which are available at ASTM’s LongAwaited Fuels and Lubricants Handbook 2nd Edition Now Available https://bit.ly/3u2e6GY.


He earned his doctorate in Chemical Engineering from The Pennsylvania State University and is a Fellow from The Chartered Management Institute, London. Dr. Shah is also a Chartered Scientist with the Science Council, a Chartered Petroleum Engineer with the Energy Institute and a Chartered Engineer with the Engineering council, UK. Dr. Shah was recently granted the honourifi c of “Eminent engineer” with Tau beta Pi, the largest engineering society in the USA. He is on the Advisory board of directors at Farmingdale university (Mechanical Technology), Auburn Univ (Tribology), SUNY, Farmingdale, (Engineering Management) and State university of NY, Stony Brook ( Chemical engineering/ Material Science and engineering). An Adjunct


Professor at the State University of New York, Stony Brook, in the Department of Material Science and Chemical engineering, Raj also has over 680 publications and has been active in the energy industry for over 3 decades. More information on Raj can be found at https:// bit.ly/3QvfaLX


Contact: rshah@koehlerinstrument.com


Dr. Vikram Mittal PhD is an Associate Professor in the Department of Systems Engineering at the United States Military Academy. His research interests include energy modeling, technology forecasting, and engine knock. Previously, he was a senior mechanical engineer at the Charles Stark Draper Laboratory. He holds a PhD in Mechanical Engineering from MIT, an MS in Engineering Sciences from Oxford, and a BS in Aeronautics from Caltech. Dr. Mittal is also a combat veteran and a major in the U.S. Army Reserve.


Vikram Mittal


Ms. Ivy Lu is part of a thriving internship program at Koehler Instrument company in Holtsville and is a student of Chemical and Molecular Engineering at Stony Brook University, Stony Brook, New York where Dr. Shah is on the external advisory board of directors.


Ivy Lu Editors Nicholas Douglas, Daniel Baek, Angelina Mae Precilla, Gavin Thomas PIN ANNUAL BUYERS’ GUIDE 2025


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