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Gear wear monitoring by modulation signal bispectrum based on motor current signal analysis

Zhang, Ruiliang, Gu, Fengshou, Mansaf, Haram, Wang, Tie and Ball, Andrew D. (2017) Gear wear monitoring by modulation signal bispectrum based on motor current signal analysis. Mechanical Systems and Signal Processing, 94. pp. 202-213. ISSN 08883270

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Gears are important mechanical components for power transmissions. Tooth wear is one of the most common failure modes, which can present throughout a gear’s lifetime. It is significant to accurately monitor gear wear progression in order to take timely predictive maintenances. Motor current signature analysis (MCSA) is an effective and non-intrusive approach which is able to monitor faults from both electrical and mechanical systems. However, little research has been reported in monitoring the gear wear and estimating its severity based on MCSA. This paper presents a novel gear wear monitoring method through a modulation signal bispectrum based motor current signal analysis (MSB-MCSA). For a steady gear transmission, it is inevitable to exist load and speed oscillations due to various errors including wears. These oscillations can induce small modulations in the current signals of the driving motor. MSB is particularly effective in characterising such small modulation signals. Based on these understandings, the monitoring process was implemented based on the current signals from a run-to-failure test of an industrial two stages helical gearbox under a moderate accelerated fatigue process. At the initial operation of the test, MSB analysis results showed that the peak values at the bifrequencies of gear rotations and the power supply can be effective monitoring features for identifying faulty gears and wear severity as they exhibit agreeable changes with gear loads. A monotonically increasing trend established by these features allows a clear indication of the gear wear progression. The dismantle inspection at 477 hours of operation, made when one of the monitored features is about 123% higher than its baseline, has found that there are severe scuffing wear marks on a number of tooth surfaces on the driving gear, showing that the gear endures a gradual wear process during its long test operation. Therefore, it is affirmed that the MSB-MSCA approach proposed is reliable and accurate for monitoring gear wear deterioration.

Item Type: Article
Uncontrolled Keywords: Motor current signal analysis; Modulation signal bispectrum; Gear wear; Gearbox monitoring
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TJ Mechanical engineering and machinery
Schools: School of Computing and Engineering
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[1]. Choy, F. K., Polyshchuk, V., Zakrajsek, J. J., Handschuh, R. F., & Townsend, D. P., Analysis of the effects of surface pitting and wear on the vibration of a gear transmission system, Tribology International. 29 (1) (1996) 77-83.
[2]. Flodin, Anders, and Sören Andersso, Simulation of mild wear in spur gears, Wear. 207 (1) (1997) 16-23.
[3]. Yesilyurt, Isa, Fengshou Gu, and Andrew D. Ball, Gear tooth stiffness reduction measurement using modal analysis and its use in wear fault severity assessment of spur gears, NDT & E International. 36(5) (2003) 357-372.
[4]. Huali Ding, DYNAMIC WEAR MODELS FOR GEAR SYSTEMS, the Ohio State University, Columbus, 2007.
[5]. Liu, Xianzeng, Yuhu Yang, and Jun Zhang, Investigation on coupling effects between surface wear and dynamics in a spur gear system, Tribology International. 101 (2016) 383-394.
[6]. Flodin, Anders, and Sören Andersson, A simplified model for wear prediction in helical gears, Wear. 249 (3) (2001) 285-292.
[7]. Osman, Thaer, and Ph Velex, Static and dynamic simulations of mild abrasive wear in wide-faced solid spur and helical gears, Mechanism and Machine Theory. 45(6) (2010) 911–924.
[8]. Wojnarowski, Jozef, and Valentin Onishchenko, Tooth wear effects on spur gear dynamics, Mechanism and Machine Theory. 38(2) (2003) 161-178.
[9]. P. J. Dempsey, Integrating oil debris and vibration measurement for intelligent machine health monitoring, Glenn Research Center, Cleveland, Tech. Rep. NASA/TM-2003-211307, 2003.
[10]. Hu, Chongqing, Wade A. Smith, Robert B. Randall, and Zhongxiao Peng, Development of a gear vibration indicator and its application in gear wear monitoring, Mechanical Systems and Signal Processing. 76 (2016) 319-336.
[11]. Ziaran, Stanislav, and Radoslav Darula, Determination of the state of wear of high contact ratio gear sets by means of spectrum and cepstrum analysis, Journal of Vibration and Acoustics. 135 (2) (2013) 021008.
[12]. Mathew, J., and J. S. Stecki, Comparison of vibration and direct reading ferrographic techniques in application to high-speed gears operating under steady and varying load conditions, Lubrication engineering. 43 (8) (1987) 646-653.
[13]. Ahmaida, Anwar, Zhen, Dong, Gu, Fengshou and Ball, Andrew, Gear wear process monitoring using acoustic signals, 21st International Congress on Sound and Vibration, Beijing, China, 2014.
[14]. SU, Heng, Mai-sheng HONG, and Shi-bo XIONG, Gears Fault Diagnosis by Monitoring Stator Current, Journal of Shanghai Jiaotong University. 34 (10) (2000):1431-1416.
[15]. Kia, Shahin Hedayati, Humberto Henao, and Gerard-Andre Capolino, Gearbox monitoring using induction machine stator current analysis, In Diagnostics for Electric Machines, Power Electronics and Drives. IEEE International Symposium on. IEEE, Cracow, Poland, (2007) 149-154.
[16]. Kia, Shahin Hedayati, Humberto Henao, and Gérard-André Capolino, A comparative study of acoustic, vibration and stator current signatures for gear tooth fault diagnosis, Electrical Machines (ICEM), XXth International Conference on. IEEE, Marseille, France, (2012) 1514-1519.
[17]. Kia, Shahin Hedayati, Humberto Henao, and Gerard-Andre Capolino, Analytical and experimental study of gearbox mechanical effect on the induction machine stator current signature, IEEE Transactions on Industry Applications. 45 (4) (2009) 1405-1415.
[18]. Kar, Chinmaya, and A. R. Mohanty, Monitoring gear vibrations through motor current signature analysis and wavelet transform, Mechanical systems and signal processing, 20(1) (2006) 158-187.
[19]. Lin, Deng-Fa, Po-Hung Chen, and Mike Williams, Measurement and Analysis of Current Signals for Gearbox Fault Recognition of Wind Turbine, Measurement Science Review. 13(2) (2013) 89-93.
[20]. Rajendra, Barshikar Raghavendra, and Santosh V. Bhaskar, Condition Monitoring of Gear Box by Using Motor Current Signature Analysis, International Journal of Scientific and Research Publications. 3 (8) (2013) 563-568.
[21]. Gu, Fengshou, Yimin Shao, N. Hu, A. Naid, and A. D. Ball, Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipment, Mechanical Systems and Signal Processing. 25 (1) (2011) 360-372.
[22]. Arthur, Neil, and Jim Penman, Induction machine condition monitoring with higher order spectra, IEEE Transactions on Industrial Electronics. 47 (5) (2000) 1031-1041.
[23]. Howard, I. M., Higher-order spectral techniques for machine vibration condition monitoring, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering. 211 (4) (1997) 211-219.
[24]. Kim, Young C., and Edward J. Powers, Digital bispectral analysis and its applications to nonlinear wave interactions. Plasma Science, IEEE Transactions on plasma science. 7(2) (1979) 120-131.
[25]. Chow, T. W. S., and Gou Fei, Three phase induction machines asymmetrical faults identification using bispectrum, IEEE Transactions on Energy Conversion. 10 (4) (1995) 688-693.
[26]. Rgeai, Mohamed Nagi, Helical gearbox fault detection using motor current signature analysis, University of Manchester, Manchester, 2007.
[27]. Chen, Zhi, Tie Wang, Fengshou Gu, Mansaf Haram, and Andrew Ball, Gear Transmission Fault Diagnosis Based on the Bispectrum Analysis of Induction Motor Current Signatures, Journal of Mechanical Engineering. 48 (21) (2012) 84-90.
[28]. Yacamini, R., K. S. Smith, and L. Ran, Monitoring torsional vibrations of electro-mechanical systems using stator currents, Journal of vibration and acoustics. 120 (1) (1998) 72-79.
[29]. F. Filippetti, G. Franceschini, C. Tassoni, AI techniques in induction machines diagnosis including the speed ripple effect, IEEE Transactions on Industry Applications. 34 (1) (1998) 98–108.
[30]. Gu, Fengshou, T. Wang, Ahmed Alwodai, Xiange Tian, Yimin Shao, and A. D. Ball, A new method of accurate broken rotor bar diagnosis based on modulation signal bispectrum analysis of motor current signals, Mechanical Systems and Signal Processing. 50 (2015) 400-413.
[31]. Powrie, H. E. G., Fisher, C. E., Tasbaz, O. D., & Wood, R. J. K., Performance of an electrostatic oil monitoring system during an FZG gear scuffing test, International Conference on Condition Monitoring, University of Wales, Swansea, UK, (1999) 145-155.
[32]. Proctor, Margaret P., Fred B. Oswald, and Timothy L. Krants. Shuttle rudder/speed brake power drive unit (pdu) gear scuffing tests with flight gears. Glenn Research Center, Cleveland,OH, United States, Tech. Rep. NASA/TM—2005-214092, 2005.
[33]. Castro, J., and J. Seabra, Global and local analysis of gear scuffing tests using a mixed film lubrication model, Tribology International. 41 (4) (2008): 244-255.
[34]. Klein, Mark Andrew, An experimental investigation of materials and surface treatments on gear contact fatigue life, The Ohio State University, Columbus, 2009.
[35]. Xue, Jian-hua, Wei Li, and Caiyan Qin, The scuffing load capacity of involute spur gear systems based on dynamic loads and transient thermal elastohydrodynamic lubrication, Tribology International. 79 (2014) 74-83.
[36]. Ganti, Venu, Yogesh Dewangan, Saurabh Arvariya, and Shyamsananth Madhavan, Influence of Micro-Geometry on Gear Scuffing, SAE Technical Paper. 2015-26-0187, 2015.
[37]. Zhang, Jiwang, Wei Li, Huaqiang Wang, Qingpeng Song, Liantao Lu, Wenjian Wang, and Zhongwei Liu, A comparison of the effects of traditional shot peening and micro-shot peening on the scuffing resistance of carburized and quenched gear steel, Wear. 368-369 (2016) 253-257.
[38]. Alwodai, Ahmed, Fengshou Gu, and A. D. Ball, A Comparison of Different Techniques for Induction Motor Rotor Fault Diagnosis, Journal of Physics: Conference Series. 364 (1) (2012) 1-11.

Depositing User: Sally Hughes
Date Deposited: 14 Mar 2017 10:30
Last Modified: 14 Mar 2017 19:28


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