Dimitrios KATERIS, Dimitrios MOSHOU, Theodoros GIALAMAS, İoannis GRAVALOS, Panagiotis XYRADAKIS

Bearing Fault Diagnosis in Mechanical Gearbox, Based on Time and Frequency - Domain Parameters with MLP-ARD

Gearboxes are one of the most important parts of the rotating machinery employed inindustries. Their function is to transfer torque and power from one shaft to another. If faults occurin any component (bearings) of these machines during operating conditions, serious consequencesmay occur. Consequently, condinuous monitoring of such subsystems could increase reliability ofmachines carrying out field operations. Recently, research has been focused on the implementationof vibration signals analysis for the health status diagnosis in gearboxes having as a base the useof acceleration measurements. Informative features sensitive to specific bearing faults and faultlocations were constructed by using advanced signal processing enabling the accuratediscrimination of faults based on their location.This work presents a fault diagnosis method for a mechanical gearbox with time and frequency -domain features by using a Multilayer Perceptron with Bayesian Automatic Relevance (MLP-ARD)Neural Network.The time and frequency-domain vibration signals of normal and faulty bearings are processed forfeature extraction. These features from all the signals are used as input to the MLP-ARD. Theexperimental results show that the proposed approach (MLP-ARD) presents very high accuracy indifferent bearing fault detection. This approach will be extended as regards real-time faultdetection of rotating parts in agricultural vehicles where the anticipation of detection of incipientfailure can lead to reduced downtime.

Anahtar Kelimeler:

Gearbox, fault detection, neural network, bearing

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Al-Balushi K. R. and B. Samanta, 2002. Gear fault diagnosis using energy-based features of acoustic emission signals, Proceedings of the I MECH E Part I Journal of Systems and Control Engineering, 216(3): 249–263.
Antoni J. and R. B. Randall, 2002. Differential diagnosis of gear and bearing faults, Transactions of the ASME: Journal of Vibration and Acoustics, 124(2): 165–171.
Bouillaut L., M. Sidahmed, 2001. Helicopter gearbox vibrations: cyclo-stationary analysis or bilinear approach? ISSPA, Kuala Lumpur, Malaysia, 13–16 August, 2001.
Heng Aiwina, Sheng Zhang,, Andy C. C. Tan, & Joseph Mathew, 2009.Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23:724-739.
Jack L.B., A.K. Nandi, 2002. Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms, Mechanical Systems and Signal Processing, 16: 373–390.
Jardine, A.K.S., D. Lin, D. Banjevic, 2006. A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mechanical Systems and Signal Processing 20: 1483–1510.
Lei, Υ., Z. Ηe, Y. Zi, 2008. A new approach to intelligent fault diagnosis of rotating machinery, Expert Systems and Applications, 36: 1593-1600. Monsen, P.T., E.S. Manolakos, M. Dzwonczyk, 1993.
Helicopter gearbox fault detection and diagnosis using analog neural networks, in: Signals, Systems and Computers, 27th Asilomar Conference, 1–3 November, 1993, 1: 381–385.
Moshou, D., D. Kateris, I. Gravalos, S. Loutridis, N. Sawalhi, Th. Gialamas, P. Xyradakis, Z. Tsiropoulos, 2010.
Determination of fault topology in mechanical subsystems of agricultural machinery based on feature fusion and neural networks. 4th International Conference TAE 2010, Czech University of Life Sciences Prague, 448-453.
Rafiee, J., F. Arvani, A. Harifi, M.H. Sadeghi, 2007. Intelligent condition monitoring of a gearbox using artificial neural network, Mechanical Systems and Signal Processing, 21: 1746-1754.
Samanta, B. and K.R. Al-Balushi, 2003. Artificial neural network based fault diagnostics of rolling element bearings using time-domain features, Mechanical Systems and Signal Processing, 17: 317–328.
Samanta, B., 2004. Artificial neural networks and genetic algorithms for gear fault detection, Mechanical Systems and Signal Processing, 18: 1273–1282.
Shiroishi, J., Y. Li, S. Liang, T. Kurfess, and S. Danyluk, 1997. Bearing condition diagnostics via vibration and acoustic emission measurements, Mechanical Systems and Signal Processing, 11 (5): 693–705.
Wilson, Q.W., F. Ismail, M.F. Golnaraghi, 2001. Assessment of gear damage monitoring techniques using vibration measurements, Mechanical Systems and Signal Processing, 15(5): 905–922.