Artificial Neural Network Classifier Design Using Genetic Algorithm and Wavelet Transform in Fault Diagnosis

Abstract

A diagnosis method basing on neural network classifier, genetic algorithm (GA) and wavelet transform is proposed for a pulse width modulation voltage source inverter. It is used to detect and identify the transistor open-circuit fault. BP neural network (BPNN) is capable of recognition. However, it has shortcomings obviously. These are just advantages of GA, which has ability of global search. Thus GA is integrated into BPNN for obtaining complementary advantages. Besides, Wavelet transform is employed as a fast and effective means analyzing the transient waveforms, as an alternative to the traditional Fourier transform. The Hybrid algorithm can offer higher detection efficiency and reliability.

Parameters Calculation and Application of Preisach Hysteresis Model Based on Combination of Genetic Algorithms and Neural Networks

“…Genetic Algorithm (GA) [11] was founded in 1962 by Hol-land, a professor from Michigan University to mimic natural genetic mechanism and evolution.It is a parallel random search optimization method and provides a generic framework for solving complex system optimization problems. Genetic Algorithm is not dependent on the problem of specific fields and has very strong robustness for the kinds of problems.…”