A Method of Adaptive Neuron Model (AUILS) and Its Application

Author

Jun, Zhai ; Xiao-jia, Yang ; Yan, Chen

Author_Institution

Sch. of Econ. & Manage., Dalian Maritime Univ.

Volume

1

fYear

2006

fDate

17-19 July 2006

Firstpage

47

Lastpage

52

Abstract

This paper presents an adaptive neuron model utilizing information of local samples - the AUILS neuron model. Differing from traditional neuron models, the AUILS neuron model fully employs the experience samples information within the local range and well embodies the association and analogy functions of cerebrum. The neural network, which is simple in structure and fast in learning speed, can realize the nonlinear mapping relationship between multi-input and multi-output. Through investigating the properties of AUILS and learning algorithm based on gradient, we build a method based on the neuron model for rotary machine fault diagnosis, which takes full advantage of expert experiences to estimate the reliability of fault existing according to the vibration signals of rotary machine in operation. It is verified that using the AUILS model, expert experiences can be well expressed in the diagnosis results

Keywords

electric machines; fault diagnosis; learning (artificial intelligence); mechanical engineering computing; neural nets; rotors; AUILS neuron model; adaptive neuron model; cerebrum; learning algorithm; neural network; nonlinear mapping relationship; rotary machine fault diagnosis; Artificial neural networks; Fault diagnosis; Feedforward neural networks; Knowledge representation; Machine learning; Management training; Neural networks; Neurons; Personnel; State estimation; Fault Diagnostics; Neuron Model; Rotary Machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Cognitive Informatics, 2006. ICCI 2006. 5th IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

1-4244-0475-4

Type

conf

DOI

10.1109/COGINF.2006.365675

Filename

4216390