Gears are significant component in a multiplicity of industrial applications such as machine tool and gearboxes. An unforeseen failure of gear may result in significant economic losses. Therefore this research propose fault detection improvement throught series of vibration signal processing techuiques. These techniques have been tested experimentally using vibration data collected from the transmission system of a CS-29 ‘Category A’ helicopter gearbox under different bearing damage severity of the second planetary stage. Results showed successful improvement of bearing fault detection

Elasha, F

Greaves, M

Mba, D

Procedia CIRP

© 2017 The Authors. Gears are significant component in a multiplicity of industrial applications such as machine tool and gearboxes. An unforeseen failure of gear may result in significant economic losses. Therefore this research propose fault detection improvement throught series of vibration signal processing techuiques. These techniques have been tested experimentally using vibration data collected from the transmission system of a CS-29 'Category A' helicopter gearbox under different bearing damage severity of the second planetary stage. Results showed successful improvement of bearing fault detection

LSBU Research Open

 Procedia CIRP  59 ( 2017 )  111 – 115 Available online at www.sciencedirect.com2212-8271 © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Peer-review under responsibility of the scientific committee of the The 5th International Conference on Through-life Engineering Services (TESConf 2016)doi: 10.1016/j.procir.2016.09.030 ScienceDirectThe 5th International Conference on Through-life Engineering Services (TESConf 2016)Bearing signal separation of commercial helicopter main gearboxFaris Elasha1, Matthew Greaves2, David Mba31Faculty of Engineering, Environment and computing, Coventry University (UK)2School of Aerospace, Transport, and Manufacturing, Cranfield University (UK)3 School of Engineering, London South Bank University (UK)* Corresponding author. Tel.: +44 247 7659139;. E-mail address: faris.elasha@coventry.ac.ukAbstractGears are significant component in a multiplicity of industrial applications such as machine tool and gearboxes. An unforeseen failure of gear may result in significant economic losses. Therefore this research propose fault detection improvement throught series of vibration signal processing techuiques.  These techniques have been tested  experimentally using vibration data collected from the transmission system of a CS-29 ‘Category A’ helicopter gearbox under different bearing damage severity of the second planetary stage. Results showed successful improvement of bearing fault detection.© 2016 The Authors. Published by Elsevier B.V.Peer-review under responsibility of the Programme Committee of the 5th International Conference on Through-life Engineering Services (TESConf 2016).Keywords: Helicopters, Gearboxes, Vibration, Diagnosis1. IntroductionAccording to industry reports, more than 50% of maintenance costs are spent on rotating equipment wear and failure [1, 2].In addition to the maintenance costs, machine downtime gravely affects the productivity and profitability of a business.  This is a common issue in various industries, including the oil and gas, power generation and water sectors. [3-6]Unsurprisingly, improving the reliability of rotating machines is one of the key challenges maintenance professionals face today.  While preventive maintenance strategies (i.e. scheduled maintenance activities) have been in use for quite some time now, it does have its drawbacks.  For example, it could call for intensive labour and long unplanned shutdowns.  More importantly, this maintenance approach does not allow end-users to fully benefit from the machine’s capability and operational life.  In other words, performing scheduled maintenance can be too early and sometimes even too late.  Even worse, it may introduce failures to the machine during the maintenance activities.A more effective approach would be to base the maintenance approach on the actual condition of the machine, rather than basing it on a pre-determined schedule.  In other words, adopting a condition-based maintenance program would be more efficient than a time-based maintenance program.  Consequently, this methodology will increase the machine’s operational life and availability, reduce equipment downtime, lower costs for labour and spare parts hence resulting in significant cost savings.  Most importantly, condition based monitoring significantly reduces the risk of catastrophic machine failure by detecting potential degradation in the respective component and making the end-user aware of this issue. [7, 8]This paper compares the effect of time synchronous averaging in improving the bearing  signal separation. For these purpose vibration data collected from a CS-29 category ‘A’ helicopters industrial test facility was used for diagnosing a © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Peer-review under responsibility of the scientifi c committee of the The 5th International Conference on Through-life Engineering Services (TESConf 2016)112   Faris Elasha et al. /  Procedia CIRP  59 ( 2017 )  111 – 115 bearing defect in the epicyclic module of helicopter main gearbox. The data was collected for various bearing fault conditions and processed using an adaptive filter and with combination of TSA algorithm to separate the non-deterministic part of the signal. The resultant signatures were then further processed using envelope analysis to extract the fault signature. [9, 10]2. Signal separation algorithmsFor this study vibration signals acquired were processed using two different paths. For the first path an adaptive filter algorithm to estimate the deterministic component of the signal was employed. Then, the wavelet and combination indicator was used to estimate the filter characteristics of the deterministic signal for envelope analysis. Lastly a frequency spectrum of the enveloped signal was determined. The second path is very similar to the first path, the only difference is the performing the TSA before signal separation, The signal processing procedures were summarised in figure 1. The signal separation was performed with an adaptive filter using fast block algorithm least mean square algorithm FBLMSdescribed by Elasha et. al [8]. The Fast Block LMS (FBLMS)algorithm was proposed to reduce the processing time [11]and as such is more suitable for online diagnostics where an instant response is required. This algorithm is based on the transforming the time signal to the frequency domain and the filter coefficient is updated on the frequency domain; details of the procedure have been summarised [12].Figure 1 Signal Processing procedures3. Time Synchronous AveragingTime Synchronous Averaging (TSA) method is one of the robust tools for analysis of machine vibration. The time synchronous average (TSA) is a technique used to separate the noise or random parts from the signal. TSA is performed by dividing the signal into segments using a synchronous signal. In the case of a rotating machine, the synchronous signal can be the pulses from the shaft tachometer. The number of points in each segment should be equal; therefore, interpolation is performed to extend the number of points in the segment. Then the segments are averaged. The technique is illustrated in figure 3 [13, 14].Figure 2 TSA layout [15]4. Experimental setupThe test was carried out on a SA 330 Puma helicopter MGB with seeded defects located at bearing outer race. Gearbox layout and parameter ratios are illustrated in Figure 3 [1]. Tests under different output power and speed conditions were performed for MGB 2nd epicyclic stage bearing with healthy, minor damaged and major damaged outer race respectively. The maximum output of 1760 kW was designed to simulate the flight regime of 100% take-off power, while a minimum output of 92 kW represented the condition of helicopter being ground-idle. Maximum speed output reached 275 rpm at second epicyclic carrier of MGB, reflecting an engine input speed of 23000 rpm. In case of signal saturation, vibration sensors with sensitivity of 10 mV/g were adopted (PCB 352C03 and Endevco 6251M4) and placed at six carefully chosen locations. Vibration signal was sampled at 51.2 kHz before anti-aliasing filtered at 25.6 kHz. Bench parameters including output torques and a 60-pulse tacho signal were recorded simultaneously for the purpose of post processing. Specific experiment setup and data collections schemes are fully described in [1].Figure 3 Gearbox internal parts [16]Raw vibration signal TSA Adaptive filter - random  signal Wavlet decomposition  Envelpe analysis Spectrum analysis Adaptive filter - random  signal Wavlet decomposition   Envelope analysis Spectrum signature  113 Faris Elasha et al. /  Procedia CIRP  59 ( 2017 )  111 – 115 To aid diagnosis all characteristic vibration frequencies were determined, see table 1. These included gears mesh frequencies of the different stages and the bearing defect frequencies for planet bearing.Table 1      Gearbox characteristic frequenciesFrequency components Frequency HZGears MeshesFirst parallel GMF Hz 8751.5Second parallel GMF 4640.94697Bevel stage GMF (Hz) 1791.242691st epicyclic stage GMF 16712nd  epicyclic stage GMF 573Faulty planet bearingBall spin 45.31426Outer race 96.69819Inner race 143.9603Cage 7.4383225. Vibration analysis prior to TSAThe measured vibration data was processed to estimate the power spectrum of the vibration signal for damaged conditions, see figure 4. This analysis was performed to assess the ability of FFT spectrum to determine the fault signature. The results show clearly that no distinctive planetary bearing fault frequency was evident in the spectrum, and it was observed that the gear mesh frequencies (GMFs) dominate the spectrum. Therefore the data was further processed using signal separation and wavelet decomposition to identify the fault signature as described earlier.Figure 4      Power spectrum of original vibration signal for the major defect conditionWavelet decomposition and combination indicator estimation were undertaken on the non-deterministic part of data sets collected from the gearbox for the different fault cases and this yielded the frequency bands and center frequencies which were then used to undertake envelope analysis. As discussed earlier the signal separation was undertaken with adaptive filter FBLMS algorithm.   Spectral plots of enveloped vibration signals following filtration, whose characteristics were determined with the aid of the wavelet decomposition, is shown in figure 5Table 2 Filter characteristics estimated based on combination indicator for all three vibration axes at 100%  maximum take-off powerCase Center frequency Fc(Hz)Band Width Bw (Hz)Combination indicatorFault-free condition 5200 266 1.95E05Minor damage  condition 6000 266 3.47E05Major damage  condition 20266 2133 7.76E05Observation from the spectra of the enveloped signal at 100% maximum continuous power, see figure 10, showed no presence of fault frequencies associated with the defective planetary bearing in the spectrum. It is apparent that the signal separation had not completely removed the gear mesh and shaft frequencies, particularly the sun gears frequencies and its harmonics for first and second epicyclic stages (38.8 and 13.2 Hz respectively), which were detected by envelope analysis, see figure 10. Existence of these frequencies is due to fact that the vibration signal used in this analysis wasn’t synchronised to any particular shaft. Figure 5 Enveloped Spectra of non-deterministic signal for a) Fault-free (b) Major (c) Minor damage (100% maximum continuous power, X direction).6. Vibration analysis after TSAThe vibration and tachometer signals acquired have been processed to build the time synchronous averaging signals, and then the non-deterministic part of TSA signal has been 1002468Frequency 100000 1000 2000 3000 4000 5000 6000 7000 8000 9000Determin power spectrum(V)(c)  (b)(a)114   Faris Elasha et al. /  Procedia CIRP  59 ( 2017 )  111 – 115 obtained using adaptive signal separation as descried earlier. Spectrum analysis of the separated signal showed no indication of the bearing failure, therefore the signal processed further using envelope analysis, the frequency bands required for envelope analysis have been obtained using wavelet decomposition  as detailed earlier. In order to detect the faults all related frequencies have been estimated as orders of rotor speed (265 RPM), see Table 3.Table 3: Frequencies in orders of rotor rotation  Frequency components Order (of rotor speed 265 RPM)Gears MeshesFirst parallel GMF Hz 1982.7Second parallel GMF 1050Bevel stage GMF (Hz) 4051st epicyclic stage GMF 378.332nd  epicyclic stage GMF129.73Faulty planet bearingBall spin 10.25Outer race 21.9Inner race 32.6Cage 1.7Observation from the spectra of the enveloped signal at 100% maximum take-off power, see Figure 11, showed existence of outer race defect frequency (21.9 orders) for both minor and major faults.  In addition inner race defect has been detected for the major fault condition at 32.6 orders. Also second harmonic of outer race (43.8 orders) has been detected for the minor fault.  Figure 6 Enveloped Spectra of TSA non-deterministic signal for a) Fault-free (b) Major (c) Minor damage (100% maximum continuous power, X direction).7. Discussion and concluionThe techniques used in this paper are typically used for applications where strong background noise masks the defect signature of interest within the measured vibration signature. The vibration signal is susceptible to background noise and in this case, the arduous transmission path from the outer race through the rollers to the inner race and then the planet carrier makes the ability to identify outer race defects even more challenging.  However the use of the signal separation has contributed significantly to improving signal-to-noise ratio.Comparisons of the results prior and after application of TSA to the vibration signal showed superiority of TSA in improving the signal separation performance leading to detection of the bearing faults for both minor and major fault conditions. While results prior to application of TSA technique showed no fault existence. Though the results after TSA showed sensitivity to measurement direction and load condition, result of measurement taken under miner fault for  Z direction showed existence of no faults for all loading conditions, where the measurement taken in X and Y direction showed existence of the minor fault.The ability of applied signal processing techniques to identify the presence of bearing fault is based on removing the masked signal and the identification of particular frequency regions with high impact energy; these impacts are due to presence of bearing defect which affect bearing sliding motion. Results of vibration analysis showed successfully detection of bearing faults using both new condition indicator and improved signal separation based on TSAIn summary an investigation employing external vibration measurement to identify the presence of a bearing defect in a CS-29 ‘Category A’ helicopter main gearbox has been undertaken. A series of signal processing techniques were applied to extract the bearing fault signature, which included adaptive filter, wavelet decomposition, TSA and envelope analysis. The combination of these techniques demonstrated the ability to identify the presence of the various defect sizes of bearing in comparison to a typical frequency spectrum. From the results presented it was clearly evident that the application of TSA prior to vibration signal separation offered a much earlier indication of damage than vibration signal  without employing of TSA.Acknowledgements: This work was sponsered by European Aviation safety agency (EASA 2012.OP.13. VHM).References[1] Elasha, F., Greaves, M., Mba, D. and Addali, A. (2015), "Application of Acoustic Emission in Diagnostic of Bearing Faults within a Helicopter Gearbox", Procedia CIRP, vol. 38, pp. 30-36.[2] Samuel, P. D. and Pines, D. J. (2005), "A review of vibration-based techniques for helicopter transmission diagnostics", Journal of Sound and Vibration, vol. 282, no. 1–2, pp. 475-508.[3] Randall, R. B., Sawalhi, N. and Coats, M. (2011), "A comparison of methods for separation of deterministic and random signals", The International Journal of Condition Monitoring, vol. 1, no. 1, pp. 11.[4] Antoni, J. and Randall, R. B. (2001), "optimisation of SANC for Separating gear and bearing signals", Condition monitoring and diagnostics engineering management, , no. 1, pp. 89-99.115 Faris Elasha et al. /  Procedia CIRP  59 ( 2017 )  111 – 115 [5] Randall, R. B. (2004), "Detection and diagnosis of incipient bearing failure in helicopter gearboxes", Engineering Failure Analysis, vol. 11, no. 2, pp. 177-190.[6] Ho, D. and Randall, R. B. (2000), "Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signal", Mechanical Systems and Signal Processing, vol. 14, no. 5, pp. 763-788.[7] Antoni, J. and Randall, R. (2006), "The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines", Mechanical Systems and Signal Processing, vol. 20, no. 2, pp. 308-331.[8] Elasha, F., Ruiz-Carcel, C., Mba, D. and Chandra, P. (2014), "A Comparative Study of the Effectiveness of Adaptive Filter Algorithms, Spectral Kurtosis and Linear Prediction in Detection of a Naturally Degraded Bearing in a Gearbox", Journal of Failure Analysis and Prevention, vol. 14, no. 5, pp. 623-636.[9] Antoni, J. and Randall, R. B. (2004), "Unsupervised noise cancellation for vibration signals: part I—evaluation of adaptive algorithms", Mechanical Systems and Signal Processing, vol. 18, no. 1, pp. 89-101.[10] Widrow, B., Glover, J. R., Jr., McCool, J. M., Kaunitz, J., Williams, C. S., Hearn, R. H., Zeidler, J. R., Eugene Dong, J. and Goodlin, R. C. (1975), "Adaptive noise cancelling: Principles and applications", Proceedings of the IEEE, vol. 63, no. 12, pp. 1692-1716.[11] Dentino, M., McCool, J. and Widrow, B. (1978), "Adaptive filtering in the frequency domain", Proceedings of the IEEE, vol. 66, no. 12, pp. 1658-1659.[12] Ferrara, E. R. (1980), "Fast implementations of LMS adaptive filters", Acoustics, Speech and Signal Processing, IEEE Transactions on, vol. 28, no. 4, pp. 474-475.[13] McFadden, P. D. (1987), "A revised model for the extraction of periodic waveforms by time domain averaging", Mechanical Systems and Signal Processing, vol. 1, no. 1, pp. 83-95.[14] McFadden, P. D. and Toozhy, M. M. (2000), "Application of Synchronous Averaging to Vibration Monitoring of rolling elements bearings", Mechanical Systems and Signal Processing, vol. 14, no. 6, pp. 891-906.[15] Sait, A. and Sharaf-Eldeen, Y. (2011), "A Review of Gearbox Condition Monitoring Based on vibration Analysis Techniques Diagnostics and Prognostics", in Proulx, T. (ed.) Rotating Machinery, Structural Health Monitoring, Shock and Vibration, Volume 5, Springer New York, , pp. 307-324, ISBN 978-1-4419-9427-1.[16] Department for Transport (2011), Report on the accident to aerospatiale (Eurrocopter) AS332 L2 Super Puma,registration G-REDL 11 nm NE of Peterhead, Scotland, on 1 April 2009 , 2/2011, Air Accident Investigation Branch, Aldershot, UK.

Bearing signal separation of commercial helicopter main gearbox

Elasha, Faris

Greaves, Matthew

Mba, David

Coventry University Pure Portal

   Bearing signal separation of commercial helicopter main gearbox  Elasha, F; Greaves, M. and Mba, D.  Published PDF deposited in Coventry University Repository   Original citation:  Elasha, F; Greaves, M. and Mba, D. (2017) Bearing signal separation of commercial helicopter main gearbox. Procedia CIRP (59), 111-115. DOI: 10.1016/j.procir.2016.09.030  http://dx.doi.org/10.1016/j.procir.2016.09.030  Elsevier  © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).  Peer-review under responsibility of the scientific committee of the The 5th International Conference on Through-life Engineering Services (TESConf 2016)  Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.  Procedia CIRP  59 ( 2017 )  111 – 115 Available online at www.sciencedirect.com2212-8271 © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Peer-review under responsibility of the scientific committee of the The 5th International Conference on Through-life Engineering Services (TESConf 2016)doi: 10.1016/j.procir.2016.09.030 ScienceDirectThe 5th International Conference on Through-life Engineering Services (TESConf 2016)Bearing signal separation of commercial helicopter main gearboxFaris Elasha1, Matthew Greaves2, David Mba31Faculty of Engineering, Environment and computing, Coventry University (UK)2School of Aerospace, Transport, and Manufacturing, Cranfield University (UK)3 School of Engineering, London South Bank University (UK)* Corresponding author. Tel.: +44 247 7659139;. E-mail address: faris.elasha@coventry.ac.ukAbstractGears are significant component in a multiplicity of industrial applications such as machine tool and gearboxes. An unforeseen failure of gear may result in significant economic losses. Therefore this research propose fault detection improvement throught series of vibration signal processing techuiques.  These techniques have been tested  experimentally using vibration data collected from the transmission system of a CS-29 ‘Category A’ helicopter gearbox under different bearing damage severity of the second planetary stage. Results showed successful improvement of bearing fault detection.© 2016 The Authors. Published by Elsevier B.V.Peer-review under responsibility of the Programme Committee of the 5th International Conference on Through-life Engineering Services (TESConf 2016).Keywords: Helicopters, Gearboxes, Vibration, Diagnosis1. IntroductionAccording to industry reports, more than 50% of maintenance costs are spent on rotating equipment wear and failure [1, 2].In addition to the maintenance costs, machine downtime gravely affects the productivity and profitability of a business.  This is a common issue in various industries, including the oil and gas, power generation and water sectors. [3-6]Unsurprisingly, improving the reliability of rotating machines is one of the key challenges maintenance professionals face today.  While preventive maintenance strategies (i.e. scheduled maintenance activities) have been in use for quite some time now, it does have its drawbacks.  For example, it could call for intensive labour and long unplanned shutdowns.  More importantly, this maintenance approach does not allow end-users to fully benefit from the machine’s capability and operational life.  In other words, performing scheduled maintenance can be too early and sometimes even too late.  Even worse, it may introduce failures to the machine during the maintenance activities.A more effective approach would be to base the maintenance approach on the actual condition of the machine, rather than basing it on a pre-determined schedule.  In other words, adopting a condition-based maintenance program would be more efficient than a time-based maintenance program.  Consequently, this methodology will increase the machine’s operational life and availability, reduce equipment downtime, lower costs for labour and spare parts hence resulting in significant cost savings.  Most importantly, condition based monitoring significantly reduces the risk of catastrophic machine failure by detecting potential degradation in the respective component and making the end-user aware of this issue. [7, 8]This paper compares the effect of time synchronous averaging in improving the bearing  signal separation. For these purpose vibration data collected from a CS-29 category ‘A’ helicopters industrial test facility was used for diagnosing a © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Peer-review under responsibility of the scientifi c committee of the The 5th International Conference on Through-life Engineering Services (TESConf 2016)112   Faris Elasha et al. /  Procedia CIRP  59 ( 2017 )  111 – 115 bearing defect in the epicyclic module of helicopter main gearbox. The data was collected for various bearing fault conditions and processed using an adaptive filter and with combination of TSA algorithm to separate the non-deterministic part of the signal. The resultant signatures were then further processed using envelope analysis to extract the fault signature. [9, 10]2. Signal separation algorithmsFor this study vibration signals acquired were processed using two different paths. For the first path an adaptive filter algorithm to estimate the deterministic component of the signal was employed. Then, the wavelet and combination indicator was used to estimate the filter characteristics of the deterministic signal for envelope analysis. Lastly a frequency spectrum of the enveloped signal was determined. The second path is very similar to the first path, the only difference is the performing the TSA before signal separation, The signal processing procedures were summarised in figure 1. The signal separation was performed with an adaptive filter using fast block algorithm least mean square algorithm FBLMSdescribed by Elasha et. al [8]. The Fast Block LMS (FBLMS)algorithm was proposed to reduce the processing time [11]and as such is more suitable for online diagnostics where an instant response is required. This algorithm is based on the transforming the time signal to the frequency domain and the filter coefficient is updated on the frequency domain; details of the procedure have been summarised [12].Figure 1 Signal Processing procedures3. Time Synchronous AveragingTime Synchronous Averaging (TSA) method is one of the robust tools for analysis of machine vibration. The time synchronous average (TSA) is a technique used to separate the noise or random parts from the signal. TSA is performed by dividing the signal into segments using a synchronous signal. In the case of a rotating machine, the synchronous signal can be the pulses from the shaft tachometer. The number of points in each segment should be equal; therefore, interpolation is performed to extend the number of points in the segment. Then the segments are averaged. The technique is illustrated in figure 3 [13, 14].Figure 2 TSA layout [15]4. Experimental setupThe test was carried out on a SA 330 Puma helicopter MGB with seeded defects located at bearing outer race. Gearbox layout and parameter ratios are illustrated in Figure 3 [1]. Tests under different output power and speed conditions were performed for MGB 2nd epicyclic stage bearing with healthy, minor damaged and major damaged outer race respectively. The maximum output of 1760 kW was designed to simulate the flight regime of 100% take-off power, while a minimum output of 92 kW represented the condition of helicopter being ground-idle. Maximum speed output reached 275 rpm at second epicyclic carrier of MGB, reflecting an engine input speed of 23000 rpm. In case of signal saturation, vibration sensors with sensitivity of 10 mV/g were adopted (PCB 352C03 and Endevco 6251M4) and placed at six carefully chosen locations. Vibration signal was sampled at 51.2 kHz before anti-aliasing filtered at 25.6 kHz. Bench parameters including output torques and a 60-pulse tacho signal were recorded simultaneously for the purpose of post processing. Specific experiment setup and data collections schemes are fully described in [1].Figure 3 Gearbox internal parts [16]Raw vibration signal TSA Adaptive filter - random  signal Wavlet decomposition  Envelpe analysis Spectrum analysis Adaptive filter - random  signal Wavlet decomposition   Envelope analysis Spectrum signature  113 Faris Elasha et al. /  Procedia CIRP  59 ( 2017 )  111 – 115 To aid diagnosis all characteristic vibration frequencies were determined, see table 1. These included gears mesh frequencies of the different stages and the bearing defect frequencies for planet bearing.Table 1      Gearbox characteristic frequenciesFrequency components Frequency HZGears MeshesFirst parallel GMF Hz 8751.5Second parallel GMF 4640.94697Bevel stage GMF (Hz) 1791.242691st epicyclic stage GMF 16712nd  epicyclic stage GMF 573Faulty planet bearingBall spin 45.31426Outer race 96.69819Inner race 143.9603Cage 7.4383225. Vibration analysis prior to TSAThe measured vibration data was processed to estimate the power spectrum of the vibration signal for damaged conditions, see figure 4. This analysis was performed to assess the ability of FFT spectrum to determine the fault signature. The results show clearly that no distinctive planetary bearing fault frequency was evident in the spectrum, and it was observed that the gear mesh frequencies (GMFs) dominate the spectrum. Therefore the data was further processed using signal separation and wavelet decomposition to identify the fault signature as described earlier.Figure 4      Power spectrum of original vibration signal for the major defect conditionWavelet decomposition and combination indicator estimation were undertaken on the non-deterministic part of data sets collected from the gearbox for the different fault cases and this yielded the frequency bands and center frequencies which were then used to undertake envelope analysis. As discussed earlier the signal separation was undertaken with adaptive filter FBLMS algorithm.   Spectral plots of enveloped vibration signals following filtration, whose characteristics were determined with the aid of the wavelet decomposition, is shown in figure 5Table 2 Filter characteristics estimated based on combination indicator for all three vibration axes at 100%  maximum take-off powerCase Center frequency Fc(Hz)Band Width Bw (Hz)Combination indicatorFault-free condition 5200 266 1.95E05Minor damage  condition 6000 266 3.47E05Major damage  condition 20266 2133 7.76E05Observation from the spectra of the enveloped signal at 100% maximum continuous power, see figure 10, showed no presence of fault frequencies associated with the defective planetary bearing in the spectrum. It is apparent that the signal separation had not completely removed the gear mesh and shaft frequencies, particularly the sun gears frequencies and its harmonics for first and second epicyclic stages (38.8 and 13.2 Hz respectively), which were detected by envelope analysis, see figure 10. Existence of these frequencies is due to fact that the vibration signal used in this analysis wasn’t synchronised to any particular shaft. Figure 5 Enveloped Spectra of non-deterministic signal for a) Fault-free (b) Major (c) Minor damage (100% maximum continuous power, X direction).6. Vibration analysis after TSAThe vibration and tachometer signals acquired have been processed to build the time synchronous averaging signals, and then the non-deterministic part of TSA signal has been 1002468Frequency 100000 1000 2000 3000 4000 5000 6000 7000 8000 9000Determin power spectrum(V)(c)  (b)(a)114   Faris Elasha et al. /  Procedia CIRP  59 ( 2017 )  111 – 115 obtained using adaptive signal separation as descried earlier. Spectrum analysis of the separated signal showed no indication of the bearing failure, therefore the signal processed further using envelope analysis, the frequency bands required for envelope analysis have been obtained using wavelet decomposition  as detailed earlier. In order to detect the faults all related frequencies have been estimated as orders of rotor speed (265 RPM), see Table 3.Table 3: Frequencies in orders of rotor rotation  Frequency components Order (of rotor speed 265 RPM)Gears MeshesFirst parallel GMF Hz 1982.7Second parallel GMF 1050Bevel stage GMF (Hz) 4051st epicyclic stage GMF 378.332nd  epicyclic stage GMF129.73Faulty planet bearingBall spin 10.25Outer race 21.9Inner race 32.6Cage 1.7Observation from the spectra of the enveloped signal at 100% maximum take-off power, see Figure 11, showed existence of outer race defect frequency (21.9 orders) for both minor and major faults.  In addition inner race defect has been detected for the major fault condition at 32.6 orders. Also second harmonic of outer race (43.8 orders) has been detected for the minor fault.  Figure 6 Enveloped Spectra of TSA non-deterministic signal for a) Fault-free (b) Major (c) Minor damage (100% maximum continuous power, X direction).7. Discussion and concluionThe techniques used in this paper are typically used for applications where strong background noise masks the defect signature of interest within the measured vibration signature. The vibration signal is susceptible to background noise and in this case, the arduous transmission path from the outer race through the rollers to the inner race and then the planet carrier makes the ability to identify outer race defects even more challenging.  However the use of the signal separation has contributed significantly to improving signal-to-noise ratio.Comparisons of the results prior and after application of TSA to the vibration signal showed superiority of TSA in improving the signal separation performance leading to detection of the bearing faults for both minor and major fault conditions. While results prior to application of TSA technique showed no fault existence. Though the results after TSA showed sensitivity to measurement direction and load condition, result of measurement taken under miner fault for  Z direction showed existence of no faults for all loading conditions, where the measurement taken in X and Y direction showed existence of the minor fault.The ability of applied signal processing techniques to identify the presence of bearing fault is based on removing the masked signal and the identification of particular frequency regions with high impact energy; these impacts are due to presence of bearing defect which affect bearing sliding motion. Results of vibration analysis showed successfully detection of bearing faults using both new condition indicator and improved signal separation based on TSAIn summary an investigation employing external vibration measurement to identify the presence of a bearing defect in a CS-29 ‘Category A’ helicopter main gearbox has been undertaken. A series of signal processing techniques were applied to extract the bearing fault signature, which included adaptive filter, wavelet decomposition, TSA and envelope analysis. The combination of these techniques demonstrated the ability to identify the presence of the various defect sizes of bearing in comparison to a typical frequency spectrum. From the results presented it was clearly evident that the application of TSA prior to vibration signal separation offered a much earlier indication of damage than vibration signal  without employing of TSA.Acknowledgements: This work was sponsered by European Aviation safety agency (EASA 2012.OP.13. VHM).References[1] Elasha, F., Greaves, M., Mba, D. and Addali, A. (2015), "Application of Acoustic Emission in Diagnostic of Bearing Faults within a Helicopter Gearbox", Procedia CIRP, vol. 38, pp. 30-36.[2] Samuel, P. D. and Pines, D. J. (2005), "A review of vibration-based techniques for helicopter transmission diagnostics", Journal of Sound and Vibration, vol. 282, no. 1–2, pp. 475-508.[3] Randall, R. B., Sawalhi, N. and Coats, M. (2011), "A comparison of methods for separation of deterministic and random signals", The International Journal of Condition Monitoring, vol. 1, no. 1, pp. 11.[4] Antoni, J. and Randall, R. B. (2001), "optimisation of SANC for Separating gear and bearing signals", Condition monitoring and diagnostics engineering management, , no. 1, pp. 89-99.115 Faris Elasha et al. /  Procedia CIRP  59 ( 2017 )  111 – 115 [5] Randall, R. B. (2004), "Detection and diagnosis of incipient bearing failure in helicopter gearboxes", Engineering Failure Analysis, vol. 11, no. 2, pp. 177-190.[6] Ho, D. and Randall, R. B. 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Bearing Signal Separation of Commercial Helicopter Main Gearbox

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