论文信息 - An Extension Neural Network and Genetic Algorithm for Bearing Fault Classification

An Extension Neural Network and Genetic Algorithm for Bearing Fault Classification

A genetic algorithm enhanced extension neural network (GA-ENN) is presented which improves on the traditional ENN by including the automatic determination of the learning rate. The GA allows the best network that produces the lowest classification error to be obtained. The effectiveness of this new system is proven using the Iris dataset. The system is then applied to the problem of bearing condition monitoring, where vibration data from bearings are analysed, diagnosed as faulty or not and their severity classified. This system is found to be 100% accurate in detecting bearing faults with an accuracy of 95% in diagnosing the severity of the fault.

[1] B. Datner,et al. Analysis of Roller/Ball Bearing Vibrations , 1979 .

[2] M. H. Wang,et al. Extension neural network and its applications , 2003, Neural Networks.

[3] Har Prashad,et al. Diagnostic monitoring of rolling-element bearings by high-frequency resonance technique© , 1985 .

[4] Chang Wook Ahn,et al. On the practical genetic algorithms , 2005, GECCO '05.

[5] Zbigniew Michalewicz,et al. Genetic Algorithms + Data Structures = Evolution Programs , 2000, Springer Berlin Heidelberg.

[6] Biswanath Samanta,et al. Bearing Fault Detection Using Artificial Neural Networks and Genetic Algorithm , 2004, EURASIP J. Adv. Signal Process..

[7] David E. Goldberg,et al. Real-coded Genetic Algorithms, Virtual Alphabets, and Blocking , 1991, Complex Syst..

[8] Joseph Mathew,et al. Multiple Band-Pass Autoregressive Demodulation for Rolling-Element Bearing Fault Diagnosis , 2001 .

[9] K. Loparo,et al. Bearing fault diagnosis based on wavelet transform and fuzzy inference , 2004 .

[10] D. E. Goldberg,et al. Genetic Algorithms in Search, Optimization & Machine Learning , 1989 .

[11] Zbigniew Michalewicz,et al. Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[12] V. Purushotham,et al. Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition , 2005 .