A Fault Diagnosis Method Based on Artificial Immune Network

Wen Ke Jiang; Yu Juan Chen; Jing Zhang

doi:10.4028/www.scientific.net/AMM.385-386.658

Paper Titles

Research on 3D Modeling System Based on Binocular Vision Image Acquisition
p.640

Design of Temperature and Humidity Monitoring System Based on Wireless
p.644

The Application Research on Underwater Riprap Recognition Using High-Density Resistivity Method
p.648

Variable Number of Multi-Sensor Fusion for Indoor RFID Tracking System
p.654

A Fault Diagnosis Method Based on Artificial Immune Network
p.658

Water Film Detection in Water Distribution Test of PCCS
p.663

An on Line Method of the Steady-State Data Acquisition
p.668

Multi-Sensor Multi-Target Data Association Algorithm Based on Swarm Intelligence
p.675

Design of Monitor System for JD15 Electro-Hydraulic Controlling Drill Rig
p.683

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 385-386A Fault Diagnosis Method Based on Artificial...

A Fault Diagnosis Method Based on Artificial Immune Network

Abstract:

A new fault diagnosis method based on artificial immune network is proposed. The network combined aiNet with radial basis function (RBF) NN. The structure of the network proposed is the same as RBF NN. The training samples are clustered first by the improved aiNet algorithm. The centers of the clustering are saved as the centers of the hidden layer, therefore, the amount and positions of nodes in the hidden layer can be determined automatically. The weight matrix is determined by least squares (LS) algorithm. The network is applied to fault diagnosis of CJK6136 spindle gear case. The results of the experiments confirm the performance of the proposed network through comparing with RBF NN under the same conditions. The diagnosis success rate for the network proposed was 99%, while that for RBF NN is 89.5%.