Condition monitoring indicators for pitting detection in planetary gear units
Francesco Berlato 1  
,  
Gianluca D'Elia 2  
,  
Mattia Battarra 2  
,  
 
 
Więcej
Ukryj
1
Bonfiglioli Riduttori S.p.A.
2
University of Ferrara
AUTOR DO KORESPONDENCJI
Mattia Battarra   

University of Ferrara
Data publikacji online: 07-01-2020
Data publikacji: 07-01-2020
Data nadesłania: 14-10-2019
Data ostatniej rewizji: 28-12-2019
Data akceptacji: 03-01-2020
 
Diagnostyka 2020;21(1):3–10
SŁOWA KLUCZOWE
DZIEDZINY
STRESZCZENIE ARTYKUŁU
In industrial field, there is an increasing demand for monitoring systems enabling predictive maintenance programs. In this context, the present work concerns the monitoring of distributed wear (pitting) in planetary gearboxes. For this purpose, some metrics of the synchronous average of the vibration signal, based on the statistical moment of the fourth order, are present in literature; in this paper, a new indicator is proposed, namely \emph{NA4mod}. The effectiveness of this metric in identifying the early stage of pitting has been evaluated by conducting an accelerated life test of about 700 hours on a test bench using a back-to-back configuration. The paper introduces the proposed metric, describes the test, presents and dis-cusses the results. Metric \emph{NA4mod} exhibits satisfactory capability to detect pitting with better reliability than other metrics in literature. In addition, the metric is shown to be sensitive to both early stage damage and pitting severity in the final stage. Results are verified by means of wavelet-transform analysis.
 
REFERENCJE (24)
1.
Smith JD. Gear Noise and Vibration. CRC Press, 1999.
 
2.
Jardine AK, Lin D, Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mechanical Systems and Signal Processing. 2006; 20(7):1483–1510. https://doi.org/10.1016/j.ymss....
 
3.
Assaad B, Eltabach M, Antoni J. Vibration based condition monitoring of a multistage epicyclic gearbox in lifting cranes, Mechanical Systems and Signal Processing. 2014;42(1):351–367. https://doi.org/10.1016/j.ymss....
 
4.
Sawalhi N, Randall RB. Gear parameter identification in wind turbines using diagnostic analysis of gearbox vibration signals, in: Advances in Condition Monitoring of Machinery in Non-Stationary Operations. 2014:273–285.
 
5.
Decker HJ, Lewicki DG. Spiral bevel pinion crack detection in a helicopter gearbox. Tech. report, NASA. 2003.
 
6.
McFadden P. Examination of a technique for the early detection of failure in gears by signal processing of the time domain average of the meshing vibration. Mechanical Systems and Signal Processing. 1987;1(2):173–183. https://doi.org/10.1016/0888-3....
 
7.
McFadden P. A technique for calculating the time domain averages of the vibration of the individual planet gears and the sun gear in an epicyclic gearbox. Journal of Sound and Vibration. 1991; 144(1):163–172. https://doi.org/10.1016/0022-4....
 
8.
McFadden PD. Detecting fatigue cracks in gears by amplitude and phase demodulation of the meshing vibration. Journal of Vibration and Acoustics. 1986; 108(2):165–170. https://doi.org/10.1115/1.3269....
 
9.
Diez-Ibarbia A, Battarra M, Palenzuela J, et al. Comparison between transfer path analysis methods on an electric vehicle. Applied Acoustics. 2017;118 (Suppl. C):83-101.
 
10.
Dalpiaz G, Rivola A, Rubini F. Effectiveness and sensitivity of vibration processing techniques for local fault detection in gears. Mechanical Systems and Signal Processing. 2000;14(3):387–412. https://doi.org/10.1006/mssp.1....
 
11.
Polyshchuk V, Choy F, Braun M. Gear fault detection with time-frequency based parameter np4. International Journal of Rotating Machinery, 2002;8 (1): 57–70.
 
12.
Battarra M, Mucchi E. Incipient cavitation detection in external gear pumps by means of vibro-acoustic measurements. Measurement, 2018;129: 51–61. https://doi.org/10.1016/j.meas....
 
13.
Decker HJ, Handshuh R, Zakrajsek J. An Enchancement to the NA4 Gear Vibration Diagnostic Parameter, Tech. report, NASA, 1994.
 
14.
ReG. D’Elia, M. Cocconcelli, R. Rubini, G. Dalpiaz, Evolution of gear condition indicators for diagnostics of planetary gearboxes, in: Proceeding of International Conference Surveillance 8, 2015.
 
15.
Samuel PD, Pines DJ. A review of vibration-based techniques for helicopter transmission diagnostics. Journal of Sound and Vibration. 2005;282(1):475–508. https://doi.org/10.1016/j.jsv.....
 
16.
Zakrajsek J. Some useful data analysis techniques for gearbox diagnostics. Tech. report, Institute of Sound and Vibration Research. University of Southampton, 1977.
 
17.
Zakrajsek J. An Investigation of Gear Mesh Failure Prediction Techniques, Tech. report, NASA. 1989).
 
18.
Randall RB. Vibration based condition monitoring, John Wiley & Sons Inc, 2011.
 
19.
Wang W, McFadden P. Application of wavelets to gearbox vibration signals for fault detection, Journal of Sound and Vibration. 1996; 192(5):927–939. https://doi.org/10.1006/jsvi.1....
 
20.
Bendjama H, Bouhouche S, Boucherit M. Application of wavelet transform for fault diagnosis in rotating machinery, International Journal of Machine Learning and Computing. 2012; 2(1):82–87.
 
21.
Martin W, Flandrin P. Wigner-Ville spectral analysis of nonstationary processes. IEEE Transactions on Acoustics, Speech, and Signal Processing. 1985;33(6):1461–1470. https://doi.org/10.1109/TASSP.....
 
22.
Staszewski W, Worden K, Tomlinson G. Time–frequency analysis in gearbox fault detection using the Wigner–Ville distribution and pattern recognition, Mechanical Systems and Signal Processing. 1997; 11(5):673–692. https://doi.org/10.1006/mssp.1....
 
23.
McClintic K, Lebold M, Maynard K, Byington C, Campbell R. Residual and difference feature analysis with transitional gearbox data, in: 54th Meeting of the Society for Machinery Failure Prevention Technology. 2000.
 
24.
Forrester D, Blunt D. Analysis of epicyclic gearbox vibration, in: Proceedings of the DSTO Third International Conference on Health and Usage Monitoring - HUMS2003. 2003;5(10).
 
eISSN:2449-5220
ISSN:1641-6414