1. Use of Acoustic Emission in Gearbox Condition Monitoring RAM 7 Workshop November 4 th & 5 th Steven Carter Roush Industries Steven.Carter@Roush.com

2. Condition Based Maintenance Condition based maintenance(CBM) is the practice of providing maintenance when needed. –Effective gearbox CBM is reliant on condition indicators(CIs). –Current primary gear CIs are vibration, temperature, and oil debris based. –Developing research is showing the benefits of acoustic emission(AE) based CIs. 2

3. Outline Vibration based condition monitoring theory. Acoustic emission condition monitoring theory. Current research on AEs applicability to gearbox condition monitoring. Theoretical comparison between AE and vibration. Case study of data collected by Roush. 3

4. Vibration Based Gearbox Condition Monitoring Collection of vibration data Development of time synchronous averaged signal Development of residual/difference signal Condition indicator calculation Vibration based indicators are typically used to measure data distribution and energy off of major meshing harmonics. Typical Condition Indicator Calculation Process –RMS –Peak Level –Normalized kurtosis –Crest factor –Peak-Peak level –Sideband levels Typical CI “Base” Metrics 4

5. Vibration Based Gearbox Condition Monitoring – Cont. Normalized Kurtosis – Kurtosis is a measure of a data sets peakedness and is normalized to reduce sensitivity to the data sets standard deviation. This measure is the basis for FM4 and NA4 1,2. Crest Factor – The ratio of a data sets peak value to its RMS level. 5

6. Vibration Based Gearbox Condition Monitoring – Cont. FM0 – The ratio of a data sets peak-to-peak level to the sum of rms levels of the main gear mesh harmonics 1. Sideband Levels – Several condition indicators exist which measure the amplitude of orders (typically first order sidebands) centered about the primary gear mesh frequency. 6

7. Acoustic Emission Theory - Background Acoustic emission is defined as transient elastic waves caused by the release of localized stress energy. Originally developed in the 1960s for static testing. Very popular in non-destructive testing: –Pipeline leak detection. –Structural health monitoring. Sensitive to crack formation in both metals and composites. Generally measured in the 100kHz to 1MHz frequency range. Relatively new to gearbox condition monitoring. 7

8. Acoustic Emission Theory - Introduction Acoustic emission can be thought of as an analog to structural vibration. Mechanical disturbance causing the release of local stress energy. Wave propagation through structure. Mechanical motion at sensor location. SourcePathReceiver Mechanical phenomena causing input force. Vibration propagation due to system dynamic response. Mechanical vibration at sensor location. Acoustic Emission Vibration Phenomena 8

9. Acoustic Emission Theory – Source AE events are caused are caused by the release of localized stress energy. Micro-cracks caused by fatigue, thermal loading, etc. Crack initiation, crack propagation, plastic deformation, impact, and friction. Shifts in the materials micro-structure are common AE sources 9

10. Acoustic Emission Theory – Path AE waves are transmitted through the structure via Lamb and Rayleigh waves. Reduced sensitivity to mechanical resonance and outside noise due to frequency band and mode type. Plate Motion for the Two Zero-order Lamb Wave Modes. Graphic, Wikipedia. Accessed October 19, 2014. http://en.wikipedia.org/wiki/Lamb_waves Rayleigh wave motion process. Graphic. Accessed October 19, 2014. http://folk.uio.no/valeriem/spice/Frame/surfacew/index.html Lamb Waves Rayleigh Wave Extensional Mode Flexural Mode 10

11. Acoustic Emission Theory – Receiver AE energy is measured through piezo-electric transducers. Transducers can have either a wideband or narrowband frequency response. Mounted in a similar manner to accelerometers. –Silicone grease is typically used as a coupling agent. Sensors come in a variety of sizes and weights. Kistler AE sensor used for testing at Roush 11

12. Acoustic Emission Theory – Measurement AE is measured via several methods. –Hit count –RMS level –Raw time data (typically recorded based off pre-set triggers) Sample rates vary up to 40MHz. Measurement locations are defined through sensitivity studies such as the pencil lead break test 7 Typical AE RMS level vs. time Measurement 12

13. AE Based Gearbox Condition Monitoring – Previous Research AE has been shown to identify faults faster then vibration 3, 5. –In one case it was noted that AE detected pitting at 8% pitted area compared to vibration detecting pitting at 30% pitted area 6. It has been shown that AE amplitude and energy increases with pitting 5, 6. Torque appears to have a minimal effect on AE rms levels; however, gearbox speed does appear to have an effect 5,6. 13

14. AE Based Gearbox Condition Monitoring – Previous Research Heterodyne-based frequency reduction technique has been successfully applied to sample AE at 100kHz 3. Toutountzakis et al 4 were able to measure the gear meshing AE transient response. –Unable to measure differences in AE data from seeded pitting. In particular, previous work by the Physical Acoustics Corporation (Mistras) has shown the success of acoustic emission when applied to a BV-107 helicopter 8. 14

15. Acoustic Emission Vs. Vibration Vibration Measurement Source Phenomena: Force variation/transmission error. Pros/Cons: Large body of research. Ease of data acquisition. Largely controlled by system dynamics. Dependent on total system vibration. Complex signal processing. Acoustic Emission Measurement Source Phenomena: Impulse from the release of localized stress energy. Pros/Cons: Insensitivity to system dynamics and total system vibration. Potential to detect faults sooner. Potentially high signal attenuation in system. Relatively small amount of research. Actual source mechanism is not fully understood 3. Potentially difficult data acquisition. Potential simplicity of signal processing. 15

16. Acoustic Emission Vs. Vibration Vibration Acoustic Emission Bottom Line: Vibration has historical prominence in gearbox condition monitoring but is fraught with difficulties. Bottom Line: AE shows significant potential but much work is still required to fully understand its use in gearbox condition monitoring. 16

17. Brake Drive Motor Roush Test Stand Gearbox Under Test 17

18. Roush Test Stand – Measurement Set-Up Kistler AE Sensor Sensor: Response: Output: Measurement Method: Kistler Model #8152B111 Wideband – 200-500kHz filtered RMS – 0.12ms time constant 60s RMS vs. time at 15-30min increments Laser Tachometer for Shaft Speed 18

19. Baseline Gearbox – AE Data 1.38HP, 7.2Nm Load Case (Estimated) 0.78HP, 4.1Nm Load Case (Estimated) AE Out (Volts) Typical AE DataAE Data Immediately Prior to Inspection ~16-20hrs of run time between these measurements. Test-stand downtime Consistent, high amplitude hits 19

20. This gear set started from a new condition and was ran intermittently for several months. Damage was found in a routine inspection after an uptick in AE activity was noticed. High wear was seen on both the ring and pinion gear faces Baseline Gearbox – Gear-Set – Post Run 20

21. Baseline Gearbox – Gear-Set – Post Run 21

22. 0.1HP, 0.9Nm Load Case (Estimated) 0.78HP, 4.1Nm Load Case (Estimated) Natural Wear Gearbox – AE Data AE Out (Volts) Test-stand downtime Consistent, high amplitude hits 22

23. This gear set started from a new condition and was ran for 5 days when significant wear was expected based on the AE data. Wear was seen on both the ring and pinion gear faces Natural Wear Gearbox – Gear-set – Post Run 23

24. Natural Wear Gearbox – Gear-set – Post Run 24

25. Roush Testing - Findings While small compared to AE hits, the RMS level is sensitive to gearbox load. AE does not appear to be sensitive to seeded faults. AE does appear to be sensitive to natural wear as exhibited in the shown data. –Wear in gear sets. –Crack in bellows coupling. –Drive motor failure. 25

26. Questions?

27. References 1.Zakrajsek James J. An Investigation of Gear Mesh Failure Prediction Techniques. NASA Technical Memorandum 102340. 1989. 2.Antolick Lance J., Branning Jeremy S., Wade Daniel R., Dempsey Paula J. Evaluation of Gear Condition Indicator Performance on Rotorcraft Fleet. American Helicopter Society 66 th Annual Forum Conference Proceedings. 2010. 3.Yongzhi Qu, He David, Yoon Jae, Van Hecke Brandon, Bechhoefer Eric, Zhu Junda. Gearbox Tooth Cut Fault Diagnostics Using Acoustic Emission and Vibration Sensors – A Comparative Study. Sensors 14, no 1. 2014. 4.Tountountzakis Tim, Keong Tan Chee, Mba David. Application of Acoustic Emission to Seeded Gear Fault Detection, NDT&E International, Volume 38, Issue 1. 2005. 5.Tountountzakis Tim, Mba David. Observations of Acoustic Emission Activity During Gear Defect Diagnosis. NDT&E International, Volume 36, Issued 7. 2003. 6.Mba David. Prognostic Opportunities Offered by Acoustic Emission for Monitoring Bearings and Gearboxes. Twelfth International Congress on Sound and Vibration. 2005. 7.ASTM Standard E976, 2010. Standard Guide for Determining the Reproducibility of Acoustic Emission Sensor Response. ASTM International. West Conshohocken, PA. 2010. DOI: 10.1520/E0976-10. www.astm.org.www.astm.org 8.Application of Acoustic Emission to Health Monitoring of Helicopter Mechanical System. Physical Acoustics Corporation. www.pacndt.com.www.pacndt.com 27