Gear faults diagnosis based on wavelet neural networks

With the development of the industry, the machine system is becoming more and more complicated, and more and more difficult to detect the gear faults of such a large and complicated system. The wavelet neural network approach is developed for gear faults diagnosis. The wavelet neural work is trained by the gradient descent optimization algorithm in this paper. The wavelet neural network based on the gradient descent optimization algorithm is used to classify the gear crack faults in the early stage. The simulated result shows that the wavelet neural network approach is effective to distinguish the state of the gear and suitable to diagnose the gear crack faults in the early stage.

[1]  Qinghua Zhang,et al.  Using wavelet network in nonparametric estimation , 1997, IEEE Trans. Neural Networks.

[2]  Bernard Delyon,et al.  Accuracy analysis for wavelet approximations , 1995, IEEE Trans. Neural Networks.

[3]  Vikram M. Gadre,et al.  Function learning using wavelet neural networks , 2000, Proceedings of IEEE International Conference on Industrial Technology 2000 (IEEE Cat. No.00TH8482).

[4]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[5]  Yagyensh C. Pati,et al.  Analysis and synthesis of feedforward neural networks using discrete affine wavelet transformations , 1993, IEEE Trans. Neural Networks.

[6]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[7]  Qinghua Zhang,et al.  Wavelet networks , 1992, IEEE Trans. Neural Networks.

[8]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[9]  S. Kaniel.  Estimates for Some Computational Techniques - in Linear Algebra , 1966 .

[10]  C. Chui,et al.  Wavelets : theory, algorithms, and applications , 1994 .

[11]  Ramesh C. Jain,et al.  A robust backpropagation learning algorithm for function approximation , 1994, IEEE Trans. Neural Networks.

[12]  Jun Zhang,et al.  Wavelet neural networks for function learning , 1995, IEEE Trans. Signal Process..