What It's About
Lubrication problems are among the most common causes of bearing failure. Yet, the actual lubrication condition during operation is still only monitored to a limited extent. A new measurement technology addresses precisely this challenge: it monitors the lubricant film directly at the point of friction, making critical operating conditions visible before they develop into damage. This opens up new possibilities for more precise maintenance, increased plant availability, and a better understanding of tribological processes in research and development.
In Conversation with Dr. Christoph Wincierz
But what does direct lubrication condition monitoring actually mean in practice? How does this approach differ from conventional condition monitoring systems, and what potential emerges when critical conditions can be detected before damage occurs? In this interview, Dr. Wincierz explains the technology, its potential applications, and the possible shift from damage diagnosis to active damage prevention.
Mr. Wincierz, many condition monitoring systems only detect problems once damage has already occurred. Why is the shift from damage diagnosis to damage prevention so crucial for the future of maintenance?
Conventional condition monitoring systems already make an important contribution by detecting damage at an early stage and reducing unplanned downtime. However, they generally only come into play once wear or material damage has already occurred. Our approach takes a crucial step further: instead of monitoring the consequences of damage, we monitor its most common cause – the lubrication condition inside the bearing.
Since around 80% of all bearing failures are attributable to lubrication problems, we can identify critical operating conditions before any physical damage occurs. In many cases, a simple corrective measure, such as relubrication or lubrication optimization, is sufficient to prevent bearing failure altogether.
Furthermore, we address a significant gap in current bearing design. Although bearing service life is calculated using established models, the actual lubrication condition throughout the entire operating life can so far only be accounted for through simplified assumptions or correction factors. Lubricant ageing, contamination, and changing operating conditions are often not adequately captured. By directly monitoring the lubrication condition, we replace these estimates with real measurement data. This transforms theoretical service life calculations into a condition-based assessment of the bearing’s actual state – and that is precisely the foundation for not only predicting damage but actively preventing it.
For companies, this means higher equipment availability, lower maintenance costs, and a genuine shift away from reactive or purely condition-diagnostic maintenance towards a preventive, data-driven maintenance strategy.
You monitor the lubrication condition directly at the point of friction. What makes this approach fundamentally different from conventional monitoring and diagnostic systems?
The key difference is that we measure directly where wear occurs – at the contact point between the bearing surfaces. Our technology uses highly precise impedance measurements to continuously determine whether a stable lubricating film is present or whether metal-to-metal contact is already occurring.
Conventional vibration sensors only detect the consequences of emerging damage, for example through material spalling or imbalance. Our sensor technology, on the other hand, detects changes in the lubricating film as early as the mixed-friction or boundary-friction regime. This provides operators with information about critical conditions much earlier, allowing them to intervene before damage occurs.
At the same time, directly measuring the lubrication condition provides information that has not previously been available through conventional condition monitoring. While traditional systems indirectly infer damage from its effects, we measure the actual root cause. This not only enables earlier warnings but also provides a much better understanding of the tribological processes within the bearing.
Furthermore, the measurement principle can be universally applied to metallic rolling and plain bearings.
A large proportion of all bearing failures are caused by lubrication problems. What potential benefits can companies gain if these issues can be detected significantly earlier?
The potential is enormous. By detecting lubrication problems at an early stage, unplanned machine downtime can be avoided and maintenance activities can be planned more effectively. Instead of replacing damaged bearings, simply adjusting the lubrication or changing the lubricant is often sufficient.
This increases equipment availability and productivity while simultaneously reducing maintenance and servicing costs. Continuous real-time monitoring also provides a reliable foundation for predictive maintenance concepts and extends the service life of bearings and machine components.
Another key advantage is that maintenance decisions will no longer have to rely on conservative assumptions or fixed maintenance intervals. Instead, companies can use the actual lubrication condition as the basis for their decisions and carry out maintenance measures precisely when they are needed. This improves cost-effectiveness while also reducing the risk of unplanned downtime.
In addition to production and maintenance, you also address research and development. What new insights can be gained through the continuous monitoring of tribological processes?
Continuous monitoring of tribological processes provides detailed insights into the behaviour of the lubricating film during actual operation for the first time. This makes it possible to precisely analyse the transitions between boundary, mixed, and full-film lubrication and evaluate them using specially developed characteristic parameters.
This opens up entirely new opportunities for research and development. On the one hand, tribological models and simulations can be validated and further improved using real measurement data. Instead of making assumptions about the lubrication condition, reliable real-time data from actual operating conditions becomes available. This significantly improves the accuracy and reliability of service life calculations, friction models, and simulation tools.
On the other hand, our sensor technology can be used throughout the entire tribological testing process – from material and lubricant development to component and test bench trials, all the way to validation under real operating conditions. This creates a continuous flow of data that accelerates development processes, improves the comparability of results, and significantly simplifies the transfer of research findings into industrial applications.
Furthermore, the data collected makes it possible to establish reliable thresholds for safe operation and specifically optimize lubrication, bearing design, and operating parameters. This means that not only production and maintenance but also research and development benefit from a significantly deeper understanding of tribological relationships.
Looking ahead, how will modern sensor technology, real-time data, and intelligent analysis transform companies' maintenance strategies in the coming years?
I am convinced that maintenance will increasingly evolve from damage diagnosis towards active damage prevention. Modern sensor technology continuously provides real-time information about the actual condition of critical machine components. Combined with intelligent data analysis, this makes it possible to identify critical developments much earlier and initiate targeted measures in good time.
An important step in this development will be for companies to base their decisions no longer exclusively on theoretical service life models or statistical failure probabilities, but on the actual operating condition of their machines. Continuous monitoring of the lubrication condition, in particular, creates an entirely new data foundation, making maintenance significantly more precise and predictable.
As a result, maintenance decisions will increasingly be based on real operating data rather than fixed intervals. This improves machine reliability and availability while simultaneously reducing costs and unplanned downtime. In the long term, such sensor data will also form the foundation for self-optimizing machines, digital twins, and the next generation of data-driven predictive maintenance concepts.
