Application and comparison of an ANN-based feature selection method and the genetic algorithm in gearbox fault diagnosis

In this paper, a system based on artificial neural networks (ANNs) was designed to diagnose different types of fault in a gearbox. An experimental set of data was used to verify the effectiveness and accuracy of the proposed method. The system was optimized by eliminating unimportant features using a feature selection method (UTA method). Consequently, the fault detection system operates faster while the classification error decreases or remains constant in some other cases. This method of feature selection is compared with Genetic Algorithm (GA) results. The findings verify that the results of the UTA method are as accurate as GA, despite its simple algorithm.

[1]  Isa Yesilyurt,et al.  Fault detection and location in gears by the smoothed instantaneous power spectrum distribution , 2003 .

[2]  Katherine T. McClintic,et al.  OF VIBRATION ANALYSIS METHODS FOR GEARBOX DIAGNOSTICS AND PROGNOSTICS , 2001 .

[3]  P. D. McFadden,et al.  Early detection of gear failure by vibration analysis--ii. interpretation of the time-frequency distribution using image processing techniques , 1993 .

[4]  Wenyi Wang,et al.  EARLY DETECTION OF GEAR TOOTH CRACKING USING THE RESONANCE DEMODULATION TECHNIQUE , 2001 .

[5]  B. Samanta,et al.  Gear fault detection using artificial neural networks and support vector machines with genetic algorithms , 2004 .

[6]  Darryll J. Pines,et al.  A review of vibration-based techniques for helicopter transmission diagnostics , 2005 .

[7]  Jian-Da Wu,et al.  Fault gear identification using vibration signal with discrete wavelet transform technique and fuzzy-logic inference , 2009, Expert Syst. Appl..

[8]  J. Utans,et al.  Input variable selection for neural networks: application to predicting the U.S. business cycle , 1995, Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr).

[9]  P. McFadden.  Interpolation techniques for time domain averaging of gear vibration , 1989 .

[10]  Ashraf Saad,et al.  Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems , 2007, Appl. Soft Comput..

[11]  T. Matsuura.  An application of neural network for selecting feature parameters in machinery diagnosis , 2004 .

[12]  L. Cohen,et al.  Time-frequency distributions-a review , 1989, Proc. IEEE.

[13]  J. Rafiee,et al.  INTELLIGENT CONDITION MONITORING OF A GEARBOX USING ARTIFICIAL NEURAL NETWORK , 2007 .