Abstract
Gears are the main components of power transmissions and are subjected to high cyclic load regime which can lead to premature fracture of the gear teeth. In order to prevent such events, research on gear condition monitoring and fault diagnostics techniques have received considerable attention. Machine learning (ML) applications have been widely combined with vibration measurement and analysis techniques for fault diagnostics in gearboxes and the majority of current techniques rely on experiments to generate training data. Despite the recognized advantages of using simulated data to train ML classifiers, this approach is still not a widespread practice. This paper proposes a simulation-driven ML framework to estimate the crack size in a gear tooth using simulated vibrations signals. Firstly, a 6-degrees-of-freedom model of a one-stage gearbox was developed to simulate the dynamic behavior of a cracked pinion. Secondly, a sample with 900 simulated vibration signals was generated considering 4 different crack sizes in the pinion tooth. Thirdly, the features of the vibration signals were extracted using 20 statistical indicators and, then, the extracted features were used to train and test 4 machine learning classifiers. Several performance evaluation metrics were computed, and the performance of the ML classifiers was compared and discussed. It was shown that the Decision Tree Classifier (DTC) performed best in the identification of small crack sizes regardless of the random selection of the training subsample.
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References
Cubillo, A., Perinpanayagam, S., Esperon-Miguez, M.: A review of physics-based models in prognostics: application to gears and bearings of rotating machinery. Adv. Mech. Eng. 8, 1–21 (2016)
Chen, H., Lu, Y., Tu, L.: Fault identification of gearbox degradation with optimized wavelet neural network 20, 247–262 (2013)
Qu, Y., He, D., Yoon, J., Hecke, B. Van, Bechhoefer, E., Zhu, J.: Gearbox tooth cut fault diagnostics using acoustic emission and vibration sensors — a comparative study. Sensors 14, 1372–1393 (2014)
Li, G., Li, F., Wang, Y., Dong, D.: Fault diagnosis for a multistage planetary gear set using model-based simulation and experimental investigation. Shock Vib. 2016, 19 (2016)
Biswal, S., George, J.D., Sabareesh, G.R.: Fault size estimation using vibration signatures in a wind turbine test-rig. Procedia Eng. 144, 305–311 (2016)
Choi, S., Li, C.J.: Estimation of gear tooth transverse crack size from vibration by fusing selected gear condition indices. Meas. Sci. Technol. 17, 2395–2400 (2006)
Saravanan, N., Ramachandran, K.I.: Expert Systems with Applications Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN). Expert Syst. Appl. 37, 4168–4181 (2010)
Chen, H., Sun, Y., Shi, Z., Lin, J.: Intelligent analysis method of gear faults based on FRWT and SVM (2016)
Jedli, L., Jonak, J.: A disassembly-free method for evaluation of spiral bevel gear assembly. Mech. Syst. Signal Process. 88, 399–412 (2017)
Mohammed, O.D., Rantatalo, M., Aidanpää, J.O.: Dynamic modelling of a one-stage spur gear system and vibration-based tooth crack detection analysis. Mech. Syst. Signal Process. 54, 293–305 (2015)
Tian, Z., Zuo, M.J., Wu, S.: Crack propagation assessment for spur gears using model-based analysis and simulation. J. Intell. Manuf. 23, 239–253 (2012)
Wang, L., Shao, Y.: Fault mode analysis and detection for gear tooth crack during its propagating process based on dynamic simulation method. Eng. Fail. Anal. 71, 166–178 (2017)
Mohammed, O.D., Rantatalo, M.: Dynamic response and time-frequency analysis for gear tooth crack detection. Mech. Syst. Signal Process. 66–67, 612–624 (2016)
Chaari, F., Baccar, W., Abbes, M.S., Haddar, M.: Effect of spalling or tooth breakage on gearmesh stiffness and dynamic response of a one-stage spur gear transmission. Eur. J. Mech. A/Solids 27, 691–705 (2008)
Saxena, A., Parey, A., Chouksey, M.: Time varying mesh stiffness calculation of spur gear pair considering sliding friction and spalling defects. Eng. Fail. Anal. 70, 200–211 (2016)
Liang, X., Liu, Z., Pan, J., Zuo, M.J.: Spur gear tooth pitting propagation assessment using model-based analysis. Chinese J. Mech. Eng. 30, 1369–1382 (2017)
Hu, C., Smith, W.A., Randall, R.B., Peng, Z.: Development of a gear vibration indicator and its application in gear wear monitoring. Mech. Syst. Signal Process. 76–77, 319–336 (2016)
Liu, X., Yang, Y., Zhang, J.: Investigation on coupling effects between surface wear and dynamics in a spur gear system. Tribol. Int. 101, 383–394 (2016)
Abouel-seoud, S.A., Dyab, E.S., Elmorsy, M.S.: Influence of tooth pitting and cracking on gear meshing stiffness and dynamic response of wind turbine gearbox. Int. J. Sci. Adv. Technol. 2, 151–165 (2012)
Del Rincon, A.F., Viadero, F., Iglesias, M., De-Juan, A., Garcia, P., Sancibrian, R.: Effect of cracks and pitting defects on gear meshing. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 226, 2805–2815 (2012)
Sobie, C., Freitas, C., Nicolai, M.: Simulation-driven machine learning: bearing fault classification. Mech. Syst. Signal Process. 99, 403–419 (2018)
Elkordy, M.F., Chang, K.C.: Neural networks trained by analytically simulated damage states. J. Comput. Civ. Eng. 7, 130–145 (1993)
Murphey, Y.L., Masrur, M.A., Chen, Z., Zhang, B.: Model-based fault diagnosis in electric drives using machine learning. IEEE/ASME Trans. Mechatron. 11, 290–303 (2006)
Chen, J., Bond, R.: Intelligent diagnosis of bearing knock faults in internal combustion engines using vibration simulation. MAMT 104, 161–176 (2016)
Er-raoudi, M., Diany, M., Aissaoui, H., Mabrouki, M.: Gear fault detection using artificial neural networks with discrete wavelet transform and principal component analysis. J. Mech. Eng. Sci. 10, 2016–2029 (2016)
Bartelmus, W.: Mathematical modelling and computer simulations as an aid to gearbox diagnostics. Mech. Syst. Signal Process. 15, 855–871 (2001)
Wu, S., Zuo, M.J., Parey, A.: Simulation of spur gear dynamics and estimation of fault growth. J. Sound Vib. 317, 608–624 (2008)
Endeshaw, H.B., Ekwaro-Osire, S., Alemayehu, F.M., Dias, J.P.: Evaluation of fatigue crack propagation of gears considering uncertainties in loading and material properties. Sustainability 9, 2200 (2017)
Tian, X., Zuo, M.J., Fyfe, K.R.: Analysis of the vibration response of a gearbox with gear tooth faults. In: Proceedings of ASME International Mechanical Engineering Congress and Exposition (IMECE 2004), ASME, Anaheim, California, USA, pp. 785–793 (2004)
Samuel, P.D., Pines, D.J.: A review of vibration-based techniques for helicopter transmission diagnostics. J. Sound Vib. 282, 475–508 (2005)
Liu, Z., Qu, J., Zuo, M.J., Xu, H.: Fault level diagnosis for planetary gearboxes using hybrid kernel feature selection and kernel Fisher discriminant analysis. Int. J. Adv. Manuf. Technol. 67, 1217–1230 (2013)
Scikit-learn: machine learning in Python — scikit-learn 0.19.1 documentation. http://scikit-learn.org/stable/
Ruiz-gonzalez, R., Gomez-gil, J., Gomez-gil, F.J., Martínez-martínez, V.: An SVM-based classifier for estimating the state of various rotating components in agro-industrial machinery with a vibration signal acquired from a single point on the machine chassis. Sensors 14, 20713–20735 (2014)
Tran, D., Mac, H., Tong, V., Tran, H.A., Nguyen, L.G.: A LSTM based framework for handling multiclass imbalance in DGA botnet detection. Neurocomputing 275, 2401–2413 (2018)
Zhao, M., Lin, J., Miao, Y., Xu, X.: Detection and recovery of fault impulses via improved harmonic product spectrum and its application in defect size estimation of train bearings. Meas. J. Int. Meas. Confed. 91, 421–439 (2016)
Zhang, Y., Tang, B., Liu, Z., Chen, R.: An adaptive demodulation approach for bearing fault detection based on adaptive wavelet filtering and spectral subtraction. Meas. Sci. Technol. 27, 25001 (2016)
Li, C., Zhang, W.E.I., Peng, G., Liu, S.: Bearing fault diagnosis using fully-connected winner-take-all autoencoder. Access. 6, 6103–6115 (2018)
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Gecgel, O., Ekwaro-Osire, S., Dias, J.P., Nispel, A., Alemayehu, F.M., Serwadda, A. (2019). Machine Learning in Crack Size Estimation of a Spur Gear Pair Using Simulated Vibration Data. In: Cavalca, K., Weber, H. (eds) Proceedings of the 10th International Conference on Rotor Dynamics – IFToMM . IFToMM 2018. Mechanisms and Machine Science, vol 61. Springer, Cham. https://doi.org/10.1007/978-3-319-99268-6_13
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