Multi neural networks based approach for fault detection and diagnosis of a DC-motor

Title:

Multi neural networks based approach for fault detection and diagnosis of a DC-motor

Creator:

Selaimia, Y., Moussaoui , A., and Abbassi, H. A.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:0d13abb0-ea9d-4e7f-8296-90bd0b5e5f3b
uuid:0d13abb0-ea9d-4e7f-8296-90bd0b5e5f3b

Subject:

Fault detection and diagnosis, multi neural network, radial basis function (RBF) neural network, and DC-motor

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Recently, neural networks have emerged as potential tools in the area of fault detection and diagnosis. This paper deals with multi neural network based fault detection and diagnosis approach. The architecture adopted is a Radial Basis function neural network (RBF). The approach is applied for detection and diagnosis of suitable parameters failures on a DC-motor based on the patterns of parameters changes. The simulation results illustrated that after training the neural networks, the system is able to detect the different motor failures.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2006 Volume:16 | Number:5

Harvested from:

CDK

Metadata only:

false