Application Research on Fault Diagnosis of the Filter Unit Based on Intelligent Algorithm of GA and WNN
Zhao Xuejun1, 2, Wang Mingfang*, 1, Wang Jie1, Tong Chuangming1, Yuan Xiujiu2
Identifiers and Pagination:Year: 2015
First Page: 922
Last Page: 926
Publisher Id: TOMEJ-9-922
Article History:Received Date: 17/02/2014
Revision Received Date: 21/03/2015
Acceptance Date: 09/06/2015
Electronic publication date: 7/10/2015
Collection year: 2015
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
This paper focuses on the potential of GA algorithm for adaptive random global search, and WNN resolution as well as the ability of fault tolerance to build a multi intelligent algorithm based on the GA-WNN model using the filter unit of analog circuit for fault diagnosis. Construction of GA-WNN model was divided into two stages; in the first stage GA was used to optimize the initial weights, threshold, expansion factor and translation factor of WNN structure; while in the second stage, initially, based on WNN training and learning, global optimal solution was obtained. In the process of using analog output signal by using wavelet decomposition, the absolute value of coefficient of each frequency band sequence was obtained along with the energy characteristics of the cross joint, with a combination of feature vectors as the input of the neural network. Through the pretreatment method, in order to reduce the neural network input, neural grid size of neurons was reduced in each layer and the convergence speed of neural network was increased. The experimental results show that the method can diagnose single and multiple soft faults of the circuit, with high speed and high precision.