Using Sound Analysis to Monitor Lubrication Condition in Greased Rolling Element Bearings

By

Mark GrangerComputational Systems, Incorporated

Email: grangerm@compsys.com Phone: (865) 675-2400 ext. 2144

Introduction

The objective of a grease lubrication program should be to optimize and maintain the lubrication condition of equipment within the program. The results of non-optimum lubrication include increased friction and loading, higher temperatures, introduction of grease into non-grease areas, susceptibility to contaminants, and eventually premature bearing damage and failure. To achieve optimum lubrication it is important to be able to determine:

the lubrication condition at any time, the conditions when re-lubrication is necessary, and the volume of grease required for re-greasing.

Unfortunately, these are not easily or commonly achieved. The objective of this paper is to evaluate the use of sound analysis as a means toward achieving optimum lubrication.

Specifically this paper will look at:

the characteristics of lubrication sounds, sound monitoring results for various lubrication conditions obtained in

controlled and industrial environments, and the practical application of sound analysis for grease lubrication monitoring.

The Characteristics of Lubrication Sounds

In rolling element bearings, lubrication sound is created by friction induced stress waves from the interaction of the roller to the race and the roller to the cage. Good roller to race interaction is rolling with elastohydrodynamic lubrication. Good roller to cage interaction is sliding with hydrodynamic lubrication. Assuming a RMS surface roughness of 0.3 micron, Figure 1 shows that the minimum lubrication film thickness for the roller to race is 1 micron or approximately 1/75 the thickness of a human hair. The minimum lubrication film thickness for the roller to cage is 10 micron. As lubrication starvation occurs the

film thickness will decrease resulting in a greater coefficient of friction. The greater coefficient of friction creates additional energy in the form of heat and sound.

Figure 1. Coefficient of friction as a function of specific film thickness.1

Figure 2 shows the sound waveform of an under-lubricated bearing. It appears like white noise and has little periodicity that is common to bearing mechanical faults. The sound is similar to rushing river or standing next to a waterfall.

Figure 2: Sound waveform from an under-lubricated bearing

In addition, the lubrication noise is most apparent at the cage speed of the bearing or one of its harmonics2.

1 Chart based on information from “Improving the Reliability of Machines by Understanding the Failure of Their Moving Parts,” Master Series Course taught at CSI by M. Neale and D. Summers-Smith, October 1997.2 “Machinery Surveillance Employing Sonic/Ultrasonic Sensors” by J. C. Robinson, J. B. Van Voorhis, K. R. Piety, and W. King, Reliability Week 1999.

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Figure 3: Spectra from an under-lubricated bearing at approximately 4000 revolutions per minute2

Sounds are distinguished by their specific tones or frequencies. For example, the piano key of ‘A’ above middle ‘C’ produces a tone at frequency of 440 Hz. The same key one octave higher produces a tone with a frequency of 880 Hz. In the industrial world specific problems can be distinguished by the frequency bandwidth where they occur. A mechanical defect in a bearing with 1” diameter ball has a tone or center frequency around 5 kHz2. For lubrication sounds, the tone or center frequency is around 30 kHz. About 75% of the energy from lubrication sound falls within one octave of this frequency. This is demonstrated in Figure 3, which shows the spectral analysis from an under-lubricated bearing running at 4000 RPM. The lubrication frequency range of 30 kHz falls outside of the human hearing range or sonic range and falls into the ultrasonic range. Therefore, for proper lubrication analysis, an instrument must be used that is capable of analyzing sounds within the lubrication frequency bandwidth. The instrument also should exclude sounds outside of the lubrication range so that lubrication sounds are not confused with sounds from other sources.

2 “Machinery Surveillance Employing Sonic/Ultrasonic Sensors” by J. C. Robinson, J. B. Van Voorhis, K. R. Piety, and W. King, Reliability Week 1999.

SonicUltrasonic

The results of this section as they apply to lubrication monitoring can be summarized as follows:

A very thin film of grease provides adequate lubrication between the roller and race,

Lubrication sound is primarily a white noise, and Lubrication sounds are most distinguishable in a single octave bandwidth

centered around 30 kHz.

Sound Monitoring Results with Controlled Lubrication Conditions

To better understand and verify theoretical assumption, a lubrication test stand was built for controlled lubrication testing. Figure 4 shows the test stand that was used to collect the controlled grease lubrication testing data.

Figure 4: Lubrication Test Stand

A motor drives a control, idler, and test bearing. Grease can be introduced into each bearing through its outer race using a zerk fitting on the bearing housing. The idler bearing can be variably loaded to 1000 pounds. The bearings tested were sealed SKF single row ball bearings with 9 balls. The bearings have an ABEC 1 rating and were driven at 1780 rpm. For each lubrication condition, peak and average dB levels were measured at three frequency ranges. The ranges were 30 kHz (lubrication range), 4 kHz (impact range), and 40 kHz (traditional ultrasonic range). The peak dB level records the highest signal

amplitude, such as in a spike, during a data collection period. The average dB level records the average of all the signal peaks during a data collection period and is least effected by spikes. The sound parameters where collected using a model 7100 SonicScanTM analyzer using a magnet mount sensor. A magnet mount sensor allowed for hands free operation and greater repeatability. The temperature was monitored on the bearing housing using a CSI Model 515 spot radiometer with laser pointer.

First to evaluate the effects on sound of over-lubrication, a new bearing running at steady-state under load was slowly greased until grease squeezed through the seals (see Figure 5).

Figure 5: Overgreased bearing

Figure 6 shows that no significant increase in sound up to twelve minutes after over-greasing. Over-greasing did create a seven-percent rise in the temperature on the outside of the bearing housing. It should be noted that the rate of grease oxidation and deterioration will increase with increasing temperature.

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Figure 6: Effects of over-greasing

Second, to evaluate the effects of under-lubrication, a new bearing with seals removed was brought to steady-state temperature under full load. The bearing was stopped long enough to wipe grease off of one side of the bearing and then re-tested. The bearing was then washed in solvent to remove all grease (see Figure 7) and then re-tested. When the bearing with no grease was approaching total failure, it was progressively re-greased and monitored for thirty minutes until it reached steady-state.

Figure 7: Bearing with grease removed

Figure 8 shows results from lubricant starvation and then re-lubrication. The dB levels increase with deteriorating lube conditions and they decrease with improving lubrication conditions. The temperature remains constant except when lubricant is absent. While temperature is a good indicator of total lube starvation, it was not a good indicator of partial lube starvation.

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Figure 8: Under-lubrication and re-greasing results

Seen from Figure 8, the initial re-lubrication made a significant improvement in sound level and temperature even though only one pump or 0.04 ounces of grease was introduced. Figure 9 shows the sound waveform (heterodyned 30 kHz audio output from SonicScan) immediately prior to and after the first pump of grease. A very distinct drop in level occurs when the grease hits the rollers. Additional greasing resulted in a minor sound level decrease and negligible effect on temperature. Permanent damage likely occurred to the bearing since the sound levels did not return to their original levels.

Figure 9: Sound waveform showing decrease in sound level when greased

The results of the sound monitoring in a controlled environment as they apply to lubrication monitoring can be summarized as follows:

Sound analysis is a poor technology for monitoring over-lubrication since over-lubrication does not significantly effect the sound parameters,

Temperature analysis is a good technology for monitoring over-lubrication since it does significantly increase with over-greasing, however, in the field be aware of process and environmental affects on the temperature,

Sound analysis is a good technology for monitoring normal and under-lubricated bearings since the sound levels increase with deteriorating conditions and decrease with improving conditions,

Lubrication problems that are not corrected in early stages will result in permanent mechanical damage, and

Listening during greasing for a decrease in the sound level is a good indicator of grease entering the bearing.

Greased

Sound Monitoring Results In Industrial Environments

Lubrication sound analysis was performed on the following operating industrial equipment: food processing conveyor bearings, 75 HP General Electric motors, and 450 HP Baldor Super E motors. The conveyor testing occurred during a single day while the motor testing occurred over a 7-month period. The testing confirmed and validated the characteristics of lubrication sound and the findings from the controlled environment testing. The instrument used to analyze the sound was a CSI Model 7100 SonicScan with a multi-frequency contact probe (see Figure 10) instead of a magnet mount sensor. Measurements were taken on the top of the zerk fitting. In general, contact probe measurements are not as repeatable as magnet mount sensor because of variable contact angles and pressures. The advantage of the contact probe is that it can easily fit into tight places and can be used on zerk fittings.

Figure 10: SonicScan with contact probe

Food Processing Conveyor Bearings

The survey included 13 bearings from a conveyor in a food processing plant. Most of the bearings were pressure washed frequently with water and greased daily. Bearing # 11 was elevated above floor level, in a difficult to reach position, and apparently not greased regularly.

Each bearing was tested three times before greasing and three times after greasing. Of all the bearings that were greased daily, the additional greasing had little or no on the bearing sound. Greasing significantly effected bearing #11. Figure 11 shows a summary of the readings taken at both the 4 kHz and 30 kHz frequency ranges. It also shows the peak (PH) and average (AV) readings at each frequency.

DB Levels for #11 Bearing Compared to the Average Bearing

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Figure 11: Conveyor Bearings Result3

For each set of columns, the first column shows the mean dB reading of all 13 bearings. The second column shows the dB reading of bearing #11 before greasing. The third column shows the dB reading of bearing #11 after greasing. The both the 4 kHz peak and 4 kHz average readings, bearing #11 was slighter higher than mean before greasing and slightly lower than mean after greasing. The 30 kHz peak reading shows a significantly elevated dB before greasing and a significant dB drop after greasing. The greatest change occurred with the 30 kHz average reading with the average reading dropping from the upper twenties to almost zero. In this test the 30 kHz average reading was the most sensitive parameter for measuring lubrication condition. It should also be noted that permanent mechanical damage was unlikely since the 4 kHz peak (mechanical impact) reading dropped to below mean and the 30 kHz average (lubrication) reading dropped to almost zero.

Headphones were used with the SonicScan to provide additional qualitative information. Unfortunately in this environment a lot of noise existed in all of the measurements. Metal cans were clattering on the conveyor, belts were sliding, and the background was loud. For these or other reasons, the headphones did not provide useful information in this study.

3 “Monitoring Lubrication Using A Multi-frequency Sonic/Ultrasonic Sensor” by Ray Garvey, Computational Systems, Incorporated.

75 HP General Electric Motors

Several 75 HP General Electric motors were monitored. The motors run at 1780 RPM and drive blowers. Each bearing has a single shield toward the rotor. The grease cavity has a zerk fitting at 2:00 o’clock and an unused grease vent plug at 10:00 o’clock. The motors are greased every 3500 hours with twelve grease gun pumps. Currently the maintenance group has no idea of the lubricant condition before or after greasing.

Figure 12 plots the lubrication sound levels according to the number of hours on the motor since greasing. Notice that the inboard bearing dB level dropped to almost zero after greasing and remained constant through 766 hours. The outboard bearing performed similarly except that the dB level began to increase after 358 hours. This could be caused by a lot of factors such as possibly only a small amount of the grease that entered the cavity actually made to it the bearing.

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Figure 12: 75 HP GE Motor, Case 1

Figure 13 shows a similar plot for a second 75 HP General Electric motor. The 30 kHz sound level performed as expected. The 4 kHz dB level seemed to not be effected by greasing. The 4 kHz dB levels very likely can be effected by noise from other machinery and processes since sonic sound travels through material much better and attenuates much slower than ultrasonic signals. The 40 kHz signal did not perform as expected. In general the 40 kHz signal is less reliable since the amount of lubrication energy created at 40 kHz is less than what is created at 30 kHz.

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Figure 13: 75 HP GE Motor, Case 2

450 HP Baldor Motor

Several 450 HP Baldor Super E motors were monitored. The motors run at 1780 RPM and drive large blowers. Each bearing is neither shielded nor sealed. The grease cavity has a zerk fitting at 12:00 o’clock and an unused grease vent plug at 6:00 o’clock. Access to the grease vents is restricted because of the design of the motor mount. The motors are greased every 3500 hours with 24 grease gun pumps. Currently the maintenance group has no idea of the lubricant condition before or after greasing.

Figure 14 plots the lubrication sound levels according to the number of hours on the motor since greasing. At 3411 hours the bearing was greased first 4 pumps and then an additional 4 pumps. With the 30 kHz readings the sound levels dropped significantly while they increased slightly with additional grease. Because of this increase greasing was stopped. Only after 552 hours, the sound level returned to its previous level that is most likely a state of adequate lubrication. Like with the GE motors, the 4 kHz sound was not effected by the greasing.

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Figure 14: 450 HP Baldor Motor, Case 1

Figure 15 charts another Baldor motor bearing. At 30 kHz this bearing has a steady sound level until 3278 hours. After greasing, the sound level dropped back close to the previous level.

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Figure 15: 450 HP Baldor Motor, Case 2

The results of the sound monitoring in an industrial environment as they apply to lubrication monitoring can be summarized as follows:

In all of the GE and Baldor bearings the 30 kHz average reading provided the most predictable readings. The 40 kHz readings generally followed the 30 kHz readings but were less consistent.

Greasing did not effect the 4 kHz readings. The 4 kHz reading can be very useful in analyzing the bearings mechanical condition or as a confirmation technique for vibration analysis.

Trying to determine lubrication condition by listening alone is ineffective since the lubrication sound is primarily white noise. Quantifiable and repeatable analysis is required to accurately assess lubrication condition. The qualitative information from listening is very useful for general listening and checking for other problems. It can also be useful during lubrication to determine when grease enters the bearings.

For all of the industrial bearings tested, optimum or baseline lubrication levels (30 kHz frequency and average sound parameter) were around 10 dB or lower. The normal operating lubrication levels were around 10 to 20 dB. For the test stands bearing and conveyor bearing the critical level before permanent damage occurred was around 30 dB. This would imply that the greasing level should occur between 20 and 30 dB. These estimates are very general. More exact levels should be determined by type of bearing and application. This could be accomplished through testing and long-term trending.

All the bearing testing did not exhibit a standard lubrication cycle except that increasing hours eventually resulted in increasing sound levels and that re-lubrication resulted in decreasing sound levels except for the case of already over-lubricated bearings. Specific lubrication cycles for specific bearings could be understood through long-term trending. This would provide much information on the real time lubrication condition and projected performance.

By trending the sound levels along with the lubrication intervals and grease amounts, the grease interval and volume can be optimized. The most favorable temperatures will be obtained when the bearing is supplied with the minimum quantity of lubricant to provide proper lubrication. Therefore as general rule, less grease is better that more grease. The trending will reveal the cause and effect of lubrication quantity on lubrication condition and interval.

Conclusion

As stated in the introduction, to achieve optimum lubrication it is important to be able to determine:

the lubrication condition at any time, the conditions when re-lubrication is necessary, and the volume of grease required for re-greasing.

As seen from the paper, sound analysis can provide immediate information about the lubrication condition while the equipment is operating. Sound analysis can also provide immediate feedback about the effectiveness of the greasing, proper greasing amount, and condition after greasing. When sound analysis is combined with good trending and monitoring, greasing intervals and amounts can be optimized. All of this result in knowing the equipment better and being better able to maintain it, extend its life, and predict its performance.