That is why viscosity and temperature sensors have become central to modern lubricant monitoring strategies.
Installed directly in machines, these sensors track lubricant condition in real time, detecting subtle changes that point to degradation or contamination. The resulting data supports smarter maintenance decisions, helping operators prevent failures, extend asset life, and avoid unnecessary downtime.
This article looks at how viscosity and temperature sensing works, why in situ monitoring is increasingly important, and how these technologies are being integrated into today’s rotating machinery.
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Role of Viscosity and Temperature in Lubrication
Viscosity and temperature together determine a lubricant’s load-carrying capacity, film thickness, and energy losses in bearings, gears, and hydraulic systems. As temperature increases, viscosity usually falls. If it drops too far, the lubricant film thins and metal-to-metal contact becomes more likely in heavily loaded contacts. At the other extreme, overly viscous oil at low temperatures can increase friction, hinder circulation, and cause poor start-up behavior.
In real operation, lubricants rarely retain their original viscosity. Thermal cycling, oxidation, contamination, and mechanical shearing all alter both viscosity and thermal response over time. Oxidation and soot buildup tend to increase viscosity, while fuel dilution or base oil breakdown can reduce it. Either shift can move the lubricant away from its intended performance window.
Because these changes occur under real operating conditions, reliable assessment depends on continuously tracking viscosity alongside temperature, rather than relying solely on periodic laboratory tests.?2,3
Why In Situ Sensing Has Become Essential?
Traditional oil analysis typically involves taking samples and sending them to a laboratory for kinematic viscosity tests, infrared spectroscopy, or elemental analysis.
While accurate, this approach introduces delays, risks of sample contamination, and offers only a snapshot in time. Rapidly developing problems can easily be missed between sampling intervals.
Modern machines complicate matters further. Variable loads, transient duty cycles, and frequent start–stop operation create sharp changes in temperature and shear rate, causing lubricant behavior to fluctuate throughout the day.
In situ viscosity and temperature sensors address these gaps by operating continuously inside lubrication circuits, sumps, or bearing housings. They allow operators to link changes in lubricant properties directly to operating events such as load shifts or environmental changes.
When connected to Industrial Internet of Things (IIoT) platforms, these measurements become primary health indicators for rotating machinery.3,4,5
Sensing Principles for Viscosity Measurements
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Viscosity sensors rely on several physical principles, each suited to particular environments and viscosity ranges.
One widely studied approach uses shear horizontal surface acoustic wave (SH-SAW) sensors. These devices measure changes in wave amplitude and phase as acoustic energy interacts with a thin layer of lubricant at the sensor surface. The response can be related to dynamic viscosity, even for relatively thin films, enabling compact solid-state sensors that fit into bearings or fluid channels.?1,6
Optical methods offer a different route. By monitoring light transmission, refraction, or scattering through flowing oil, these sensors infer viscosity indirectly through changes in oil composition. Because they can also detect contamination and wear debris, optical systems often provide richer diagnostic insight.
Electrical techniques add another layer. Triboelectric and dielectric sensors track changes in charge transfer or permittivity caused by oxidation, additive depletion, or contamination. In some cases, these electrical signals also correlate with viscosity changes, supporting early fault detection.4,7
Temperature as Context and a Warning Signal
Temperature measurement is essential for interpreting viscosity data. Since viscosity is highly temperature-dependent, accurate local temperature readings are necessary to compare sensor outputs with reference curves or models. This is particularly important in components such as turbochargers or heavily loaded bearings, where temperatures can change rapidly.
Thermocouples, resistance temperature detectors (RTDs), and semiconductor sensors are commonly embedded in bearing rings, housings, or lubricant lines.
Beyond providing context for viscosity readings, temperature also serves as a direct indicator of abnormal conditions. Elevated temperatures can signal boundary lubrication, misalignment, or restricted oil flow, all of which accelerate oxidation and lubricant degradation.1,2,5
Advanced sensor systems combine viscosity and temperature measurements with algorithms that distinguish between reversible thermal effects and true chemical breakdown, improving diagnostic reliability.2,4
Integration in Smart Bearings and Machinery
Smart bearings incorporate sensor modules that monitor temperature, vibration, and lubricant condition during operation. Typically mounted in the bearing housing or outer ring, these sensors often communicate wirelessly, simplifying installation in confined spaces.
Within this architecture, viscosity sensors provide insight into film formation in the load zone, while temperature data reflects duty cycles and cooling effectiveness. Recent developments in self-powered smart bearings are especially promising. By harvesting energy from rotation, vibration, or temperature gradients, these systems can power low-energy sensors and wireless transmitters without external wiring.1,5,8
Such capabilities are particularly valuable for remote or difficult-to-access assets, including offshore wind turbines and rail systems. With onboard processing, smart bearings can evaluate data locally and transmit only relevant health information to higher-level monitoring systems.5,9
Online Monitoring Architectures in Engines and Gearboxes
Automotive and industrial engines increasingly use online oil monitoring modules that continuously sample lubricant from galleries or sumps and return it to the system. These units combine viscosity sensors, based on acoustic, microfluidic, or capacitive principles, with temperature probes for compensation.
When linked to engine control units, the data supports adaptive oil change intervals, improved cold-start strategies, and load optimization based on actual lubricant condition rather than fixed schedules.
Gearboxes and hydraulic systems benefit in similar ways. Sensors installed in return lines or critical flow paths can detect viscosity increases caused by oxidation, water ingress, or contamination. Early warnings of filter blockage, cavitation, or excessive wear can then be passed to SCADA systems or cloud platforms for fleet-level analysis.2-4,10
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Benefits for Predictive Maintenance and Asset Management
Together, viscosity and temperature sensing enable a shift from time-based to condition-based maintenance. By tracking how viscosity evolves with temperature and load, maintenance teams can spot emerging problems, such as lubricant breakdown or impending bearing damage, well before failure occurs.
Timely interventions, including filtration, partial oil replacement, or operating adjustments, reduce downtime and extend component life. Over the long term, asset managers can use aggregated data across multiple machines to identify trends linked to specific lubricants or operating conditions, supporting better specification decisions and warranty management..3,4,5
Emerging Sensor Technologies for Lubricant Monitoring
Sensor development continues to push lubricant monitoring toward more compact, autonomous, and information-rich systems. Self-powered triboelectric sensors are a notable example. By exploiting fluid motion and contact electrification, these devices generate electrical signals that change with oxidation state, additive content, and, in some cases, viscosity - without requiring an external power source.4,5
Acoustic techniques are also advancing. Improved SH-SAW devices and guided-wave methods are being refined to measure viscosity and film thickness directly at lubricated contacts. At the same time, miniaturized infrared spectrometers and MEMS-based emitters and detectors are extending spectroscopic analysis into embedded platforms, allowing simultaneous tracking of viscosity, oxidation, and contamination.
As these technologies mature, viscosity and temperature sensing will play an even larger role in keeping critical machinery reliable.1,11,12
References and Further Reading
- Tyreas, G. et al. (2022). Measuring lubricant viscosity at a surface and in a bearing film using shear-horizontal surface acoustic waves. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology. DOI:10.1177/13506501221092867. https://journals.sagepub.com/doi/full/10.1177/13506501221092867
- Nour, M. A. et al. (2020). A Review of the Real-Time Monitoring of Fluid-Properties in Tubular Architectures for Industrial Applications. Sensors, 20(14), 3907. DOI:10.3390/s20143907. https://www.mdpi.com/1424-8220/20/14/3907
- Wang, J., & Mo, Y. (2022). Online Monitoring of Automotive Engine Lubricating Oil Based on Internet of Things Technology. Wireless Communications and Mobile Computing, 2022(1), 2478186. DOI:10.1155/2022/2478186. https://onlinelibrary.wiley.com/doi/10.1155/2022/2478186
- Zhao, J. et al. (2021). Real-Time and Online Lubricating Oil Condition Monitoring Enabled by Triboelectric Nanogenerator. ACS Nano, 15, 7, 11869–11879. DOI:10.1021/acsnano.1c02980. https://pubs.acs.org/doi/10.1021/acsnano.1c02980
- Zhang, Y. et al. (2023). A comprehensive review of self-powered smart bearings. Renewable and Sustainable Energy Reviews, 183, 113446. DOI:10.1016/j.rser.2023.113446. https://www.sciencedirect.com/science/article/abs/pii/S1364032123003039
- Kobayashi, S., & Kondoh, J. (2020). Feasibility Study on Shear Horizontal Surface Acoustic Wave Sensors for Engine Oil Evaluation. Sensors, 20(8), 2184. DOI:10.3390/s20082184. https://www.mdpi.com/1424-8220/20/8/2184
- Liu, Z. et al. (2022). Oil debris and viscosity monitoring using optical measurement based on Response Surface Methodology. Measurement, 195, 111152. DOI:10.1016/j.measurement.2022.111152. https://www.sciencedirect.com/science/article/abs/pii/S0263224122004109
- Smart bearings: the smart choice for predictive maintenance. (2021). Engineering Update. https://engineering-update.co.uk/2021/12/16/smart-bearings-the-smart-choice-for-predictive-maintenance/
- Gong, L. et al. (2025). Self-powered technologies for smart bearings. Fundamental Research. DOI:10.1016/j.fmre.2025.04.007. https://www.sciencedirect.com/science/article/pii/S2667325825002067
- Karluk, A. A. et al. (2022). Online lubricant degradation monitoring using contact charging of polymers. Applied Surface Science, 584, 152593. DOI:10.1016/j.apsusc.2022.152593. https://www.sciencedirect.com/science/article/abs/pii/S0169433222001775
- Chmelar, J. et al. (2020). Experimental study of lubrication film monitoring in a roller bearing by utilization of surface acoustic waves. Tribology International, 141, 105908. DOI:10.1016/j.triboint.2019.105908. https://www.sciencedirect.com/science/article/abs/pii/S0301679X1930427X
- Bley, T. et al. (2016). Degradation monitoring of aviation hydraulic fluids using non-dispersive infrared sensor systems. Sensors and Actuators B: Chemical, 224, 539-546. DOI:10.1016/j.snb.2015.10.049. https://www.sciencedirect.com/science/article/abs/pii/S0925400515305153
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