Grease Degradation Diagnosis by App

Mobile App for on-site analysis of lubricant condition in bearings and linear motion systems

  • Grease Degradation Diagnosis by App
    Grease Degradation Diagnosis by App

A broad spread of machinery and equipment, from machine tools to railcars, utilise grease-lubricated products like bearings, ball screws and linear guides. However, grease degrades over time as machinery operates, so on-site lubricant inspection is vital to help maximise uptime and OEE (overall equipment effectiveness). In recent years, more and more companies are switching their equipment maintenance strategy from time-based maintenance to condition-based maintenance. A need therefore exists for a grease degradation diagnosis method capable of rapid and highly accurate deployment on site. Such an app would also help combat labour shortages and support carbon neutrality efforts.
Existing high-accuracy diagnosis methods rely on laboratory analysis, but this comes with high cost, a lack of suitability for on-site use, and a long wait for results. Visual observation or densitometer methods are thus more common but suffer from low accuracy. Consequently, many companies replenish lubricant unnecessarily early to ensure stable operation, resulting in wasteful grease usage.
With these thoughts in mind, NSK set about developing a far more convenient, cost-effective, fast and accurate method of grease degradation diagnosis. Importantly, the new NSK app can analyse the level of lubricant degradation using just a small sample of grease. Chemical changes to the base oil and additives of the grease occur due to heat from operation and oxidation with the passage of time. These chemical changes cause the molecular structure of the lubricant to change and absorb more short-wavelength light, affecting its colour. Grease itself starts off white when new, turning yellow or orange with use and eventually becoming black, which indicates zero remaining life. The NSK app quantifies grease by its colour to calculate remaining life.