ACO Trained ANN-Based Intelligent Fault Diagnosis

Hua Wang Shi

doi:10.4028/www.scientific.net/AMM.20-23.141

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 20-23ACO Trained ANN-Based Intelligent Fault Diagnosis

ACO Trained ANN-Based Intelligent Fault Diagnosis

Abstract:

By use of the properties of ant colony algorithm and particle swarm optimization, this paper presents an application of an Ant Colony Optimization (ACO) algorithm and artificial neural network (ANN) to fault diagnosis. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behavior of real ants in nature searching for food. Neural network is used to express the nonlinear function between the input and output of the fault diagnosis of the rolling bearing. And ant colony optimization (ACO) algorithm is used to learn NN. The new algorithm has the merits of both ACO algorithm and neural network. It also provides a new way for the fault diagnosis through constructing the intelligent model by ant colony-neural network and overcomes the shortcomings of traditional algorithm.

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Periodical:

Applied Mechanics and Materials (Volumes 20-23)

Pages:

141-146

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.20-23.141

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Online since:

January 2010

Authors:

Hua Wang Shi

Keywords:

Ant Colony Optimization (ACO), Artificial Neural Network (ANN), Fault Diagnosis

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