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Adaptive Particle Swarm Optimization with Neural Network for Machinery Fault Detection

By B. Kishore, M. R. S. Satyanarayana and K. Sujatha

Abstract

Abstract — Rotating machines are one of the most important elements in almost all the industries and continuous condition monitoring of these crucial parts is essential for preventing early failure, production line breakdown, improving plant safety, efficiency and reliability. Faults may also be developed over a long period of time or even suddenly. However manual fault detection techniques are error prone. This paper identifies and utilizes the distribution information of the population to estimate the evolutionary states. Based on the states, Adaptive control strategies are developed for the inertia weight and acceleration coefficients for faster convergence speed. The Particle Swarm Optimization (PSO) is thus systematically extended to Adaptive Particle Swarm Optimization (APSO), so as to bring about outstanding performance when solving global optimization problems. This paper proposes an adaptive particle swarm optimization with adaptive parameters. Adaptive control strategies are developed for the inertia weight and acceleration coefficients for faster convergence speed

Topics: Optimization (PSO
Year: 2014
OAI identifier: oai:CiteSeerX.psu:10.1.1.412.9498
Provided by: CiteSeerX
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