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

Yıl 2014, Cilt: 10 Sayı: 2, 101 - 106, 01.04.2014

Dimitrios Katerıs Dimitrios Moshou Theodoros Gıalamas İoannis Gravalos Panagiotis Xyradakıs

Öz

Gearboxes are one of the most important parts of the rotating machinery employed in

industries. Their function is to transfer torque and power from one shaft to another. If faults occur

in any component (bearings) of these machines during operating conditions, serious consequences

may occur. Consequently, condinuous monitoring of such subsystems could increase reliability of

machines carrying out field operations. Recently, research has been focused on the implementation

of vibration signals analysis for the health status diagnosis in gearboxes having as a base the use

of acceleration measurements. Informative features sensitive to specific bearing faults and fault

locations were constructed by using advanced signal processing enabling the accurate

discrimination 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 for

feature extraction. These features from all the signals are used as input to the MLP-ARD. The

experimental results show that the proposed approach (MLP-ARD) presents very high accuracy in

different bearing fault detection. This approach will be extended as regards real-time fault

detection of rotating parts in agricultural vehicles where the anticipation of detection of incipient

failure can lead to reduced downtime.

Anahtar Kelimeler

Gearbox, fault detection, neural network, bearing

Kaynakça

• 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.