Managing the Health and High Costs of Robotics Using Grease Sampling and Analysis

Lisa Williams and Richard Wurzbach MRG Labs

Automotive, food production and other manufacturers have sought to develop methods to evaluate the condition of grease in robots. Periodic sampling and analysis of the grease from these components can provide robot owners a clearer picture of robot joint health, determine grease condition for optimal changeout periods and pinpoint latent issues that can be addressed prior to failure. Monitoring a few key data points, such as wear, consistency and color, may allow the owner to transition from calendar-based to condition-based change outs. This has the potential to save hundreds of thousands of dollars per year for an owner of a large fleet of robots. This paper will discuss grease sampling and analysis as a solution to optimize grease life, identify emerging problems and intervene to correct potential problems before significant damage or failure occurs.

1. Grease Sampling

In most circumstances, procedures for obtaining grease samples from bearing housings and gears are not consistent and likely do not represent the true condition of the “active” grease near the lubricated surface. Therefore the challenge in optimizing a grease analysis program is the development of test methodologies to measure in-service grease conditions utilizing a smaller amount of grease and a sampling process that enables representative grease samples be taken without disassembling of the component. In this new design, the sampling fitting is also optimized for the subsequent laboratory analysis found in ASTM D7918.

Robotics applications provide an excellent opportunity for the passive grease sampling device described in ASTM D7718 to be used. Due to the low consistency (high penetration value) of the grease in these applications, the passive grease sampling device can be threaded into the joint locations and used as a syringe to pull the grease from the location and send to the lab for routine analysis. In other cases, a syringe and tubing may compliment the passive grease sampling device and enable a standard grease volume to be obtained even from the difficult J1 position.

2. Grease Analysis

The following tests make up a streamlined grease analysis evaluation for robotics per ASTM D7918.

2.1. Ferrous Debris Monitoring

This method utilizes a faraday-effect sensor to minimize data scatter due to particle distribution issues and improves trendability and sensitivity of results. The ferrous particles detected in this device are particle

size independent. The gear contents in the robotics are steel, making this instrument a very useful tool in the trending of wear particles. Per EPRI published research report 102-0247 Effective Grease Practices, the fdm+ test showed the lowest standard deviation when compared to other comparable wear measurement tests.

2.2. Grease Colorimetry

Appearance changes in grease including darkening and unexpected or mixed colors are often the first condition noted that may indicate unusual lubrication conditions or mixing. The grease colorimetry optical cell is designed to create an optical path for the i-Lab visible light spectrometer, and includes a sliding drawer that presents the extruded grease on a thin film substrate for introduction into the optical light path of the i-Lab spectrometer. Vigo and Moly White greases are the two most common greases used in robotics applications. Each have unique signatures and additive packages, allowing them to be differentiated and identified if mixed.

2.3. Additional Testing

If concerns arise during the above trending and screening process, follow-up analysis can be performed using advanced test methods such as grease consistency, FT-IR, anti-oxidants, metals spectroscopy, analytical ferrography, patch microscopy and advanced rheology.

2.3.1. Grease Consistency

As outlined in D7918, the grease is measured under varying load conditions during the extraction of the grease through the extrusion die, the consistency of the grease can be compared to the new grease consistency. Changes in this value, whether indicating a thinning or thickening of the grease, can be used to flag this property. Mixtures of grease can be identified by changes in grease consistency. Followup detailed analysis with a rheometer can further classify the condition of the grease and relate to such parameters as dropping point and cone penetrometer, based on earlier studies by Nolan and Sivik [1] and Johnson [2]

2.3.2. FTIR Spectroscopy

FTIR spectra are created from new grease samples for all greases in a facility’s program. Then the sampled in-service greases are tested and compared to the spectra of new grease. In particular, for different families of greases, the FTIR spectra are quite different and can be compared to see if significant mixing has occurred.

2.3.3. Anti-Oxidants

The RULER instrument works on the principle of linear sweep voltammetry. By applying this test method, in which a variable voltage is applied to the sample while measuring the current flow, the presence and concentration of various antioxidant additives (including, but not limited to ZDDP) can be determined based on their unique electrochemical oxidation potential and the magnitude of the induced current. Monitoring residual anti-oxidants in purge greases can provide feedback on the effectiveness of grease relubrication frequencies. Vigo and Moly White greases contain different additive packages which are detectable with the RULER technology. Different additive packages in greases often are not compatible and can lead to the grease not functioning optimally at that location.

2.3.4. Metal Spectroscopy

The grease is weighed out and added to a glass vial where it is diluted and dissolved with a filtered mixture of grease solvent. This liquid mixture is then analyzed by RDE (Rotating Disc Electrode) or ICP (Inductively Coupled Plasma) spectroscopy, and the results are ppm normalized to 1 gram of grease based on the measured weight of grease that was dissolved. The concentration of metals in the grease can be compared to the new grease for the purpose of identifying significant differences in additive metals that could point toward grease mixing. Also, the presence of wear metals can be deduced.

3. Application

Robots have, in recent years, been playing an ever expanding role in manufacturing, ranging from small, precise parts placement and assembly, to larger payloads and activities, including automotive applications. Some automotive manufacturers have sought to develop methods to evaluate grease sampling and analysis methods for suitability of monitoring these robots that can have a significant impact on reliability. Periodic sampling and analysis of the grease from these components can provide the robot owner with a clearer picture of robot joint health, and pinpoint latent issues that can be planned and addressed prior to failure. In Figure 2, an automotive manufacturer evaluated a fleet of robots for ferrous wear levels. Charting the ppm Fe and comparing to the averages, the robot owners were able to identify areas of excessive wear as compared to the entire fleet and initiate grease changes or additional maintenance activities in those areas.

Cost Savings

Grease sampling and analysis may be a solution to address greased component reliability, to avoid unexpected failures, identify emerging problems, and even intervene to correct potential problems before significant damage occurs. This is especially important for manufacturing applications where unexpected downtime has a significant economic impact. Looking at a typical facility containing 500 robots, the cost savings by implementing grease analysis on the 500 robots is significant. A typical 5 gallon bucket of grease can range between $400-$500. Each robot requires one new 5 gallon bucket of grease each year. In a plant containing 500 robots, the cost to replace the grease alone is $200,000 per year.

What if we could extend the life the grease for 1 year through grease analysis? A typical grease sampling kit per ASTM D7718 costs $125. One kit can evaluate all 6 joint locations on the robot. Using routine analysis, the robot owner can assess the condition of the robots for $62,500 and potentially extend the grease life for one year or longer, saving the company $137,500 in grease costs in one year. can be used to further evaluate the condition of the grease. The RULER graph shows a significant amount of anti-oxidant protection remains in the grease and the consistency of the grease remains satisfactory. Since the wear levels are low and the physical properties of the grease are sufficient, it is recommended the grease stay in service. Relubrication is not necessary at this time. By evaluating the grease using basic analysis tests and demonstrating it is still in optimal condition, a costly grease purge and replace was avoided and the life of the current grease was extended.

4. Conclusion

Grease analysis presents a significant opportunity to expand machinery diagnostic capabilities. The historical challenges of obtaining representative and trendable samples are being addressed through technological developments and new approaches. The further development of repeatable analysis methods that utilize smaller quantities of grease will produce greater value, and encourage the sampling of greases from locations where reliability is important. By designing grease sampling equipment appropriately, the matter of optimal grease replenishment may also be addressed through the establishment of sampling programs. Wherever there is a critical machine, regardless of lubricant type, the demand for reliability drives the development of

improved sampling methods and analysis techniques to produce the valuable information present in lubricant analysis. The integration of multiple diagnositic technologies, such as Infrared, Vibration Analysis, Motor Circuit Monitoring, and Lubricant analysis (both oil and grease) is a proven best practice approach to improving machinery reliability and getting the most from investment in diagnostic monitoring.

List of References

[1] Nolan, S., Sivik, M., “The Use of Controlled Stress

Rheology to Study the High Temperature Structural

Properties of Lubricating Greases,” NLGI 71st

Annual Meeting, Dana Point, CA, 2004. [2] Johnson, B., “The Use of a Stress Rheometer in Lieu of Cone Penetration,” NLGI 74th Annual Meeting,

Scottsdale, AZ, 2007. [3] Wurzbach, R., “Streamlined Grease Sampling and Analysis for Detection of Wear,Oxidation and Mixed Greases”, NLGI Annual Meeting,

Williamsburg, VA, USA, June 2008. [4] Wurzbach, R., Williams, L., Doherty, W., “Methods for Trending Wear Levels in Grease Lubricated

Equipment”, Society of Tribologists and Lubrication

Engineers (STLE) Annual Meeting, Las Vegas, NV,

USA, May 2010. [5] Electric Power Research Institute, “Effective Grease

Practices”, Report #1020247, Palo Alto, CA, USA,

October 2010. [6] Wurzbach, R., Bupp, E., Hart, J., “New methods of grease sampling and analysis for motor operated valves”, Motor Operated Valve Users Group

Conference, San Antonio, TX, USA, January 2011. [7] Wurzbach, R., Williams, L., Bupp, E., “Grease

Sampling and Analysis for Wind Turbines and other Bearing and Gear Applications”, ReliablePlant

Conference, Indianapolis, IN, USA, May 2012.

- 31 NLGI SPOKESMAN, JANUARY/FEBRUARY 2017