Fault Detecting Technology Based on BP Neural Network Algorithm

Authors:
Ran Jin;Kun Gao;Zhigang Chen;Chen Dong;Yanghong Zhang;Lifeng Xi
Affiliations:
School of Computer Science And Information Technology, Zhejiang Wanli University, Ningbo, Zhejiang 315100, P.R. China;School of Computer Science And Information Technology, Zhejiang Wanli University, Ningbo, Zhejiang 315100, P.R. China;School of Computer Science And Information Technology, Zhejiang Wanli University, Ningbo, Zhejiang 315100, P.R. China;School of Computer Science And Information Technology, Zhejiang Wanli University, Ningbo, Zhejiang 315100, P.R. China;School of Computer Science And Information Technology, Zhejiang Wanli University, Ningbo, Zhejiang 315100, P.R. China;School of Computer Science And Information Technology, Zhejiang Wanli University, Ningbo, Zhejiang 315100, P.R. China
Venue:
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Year:
2007

Citing 2
Cited 0

Fault diagnosis in power plant using neural networks

Information Sciences: an International Journal - Intelligent manufacturing and fault diagnosis (II). Soft computing approaches to fault diagnosis
Backpropagation Artificial Neural Network Detects Changes in Electro-Encephalogram Power Spectra of Syncopic Patients

Journal of Medical Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes an automatic online detecting system. In the system, digital image processing technology is used to preprocess X-ray images of the products, and neural network algorithm is applied to diagnose faults. The fault recognition model adopts an improved back-propagating neural network, which is trained by a series of standard X-ray images of correctly assembled products. The detecting system combines digital radiography technology with digital image processing, and applies the back-propagating neural network algorithm in the fault recognition process. The system improves the speed and reliability of fault detection and has application in the field of industrial nondestructive detection.