Fault diagnosis of nonlinear systems based on hybrid PSOSA optimization algorithm

Li, Ling-Lai; Zhou, Donghua

doi:10.1007/s11633-007-0183-4

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…where m is the number of ants in the colony, θ is a parameter that balances the value of the cost and time, TCp and TTp represent the resulting total cost and time for path P. When an ant travels through the network, the probabilistic decision rule, pij, that the ant will choose the jth resource at node i, i.e., rij, is represented by (2).…”

Section: P-aco Approach To Solving the Problemmentioning

confidence: 99%

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A Meta-Heuristic with Ant Colony Approach to Complex System

Liu

Cao

Yuan

2010

AMM

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This paper proposes a new approach to determining the complex system design for a product mix comprising complex hierarchies of subassembly and components. Pareto Ant Colony Optimisation as an especially effective meta-heuristic for solving the problem of complex system design was introduced in this paper. A Pareto Optimal Set of complex system in which only the non dominated solutions allow ants to deposit pheromones over the time and cost pheromone matrices after certain generation runs. Simulation results show that the model for complex system and the hybrid algorithms are effective to the design of complex system.

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Section: P-aco Approach To Solving the Problemmentioning

confidence: 99%

“…It is very helpful to design an algorithm according to the property of the problem incorporated with the performance analysis and the structure design of intelligent optimization algorithm [1] [2]. It is also necessary to pay particular attention to the rapid heuristic method by extracting the feature information of the problem.…”

Section: Introductionmentioning

confidence: 99%

A Meta-Heuristic with Ant Colony Approach to Complex System

Liu

Cao

Yuan

2010

AMM

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“…Li et al [5] proposed a fault diagnosis of nonlinear systems based on intelligent optimization algorithm (PSOSA). The PSOSA could avoid premature convergence of standard particle swarm optimization (PSO) by introducing the probabilistic jumping property of simulated annealing (SA).…”

Section: Introductionmentioning

confidence: 99%

Study on a New Fault Diagnosis Method Based on Combining Intelligent Technologies

Li¹,

Zhou²

2016

IJMUE

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In allusion to more indeterminate information and higher speed request characteristic in fault diagnosis system, according to the intelligence complementary strategy, a new fault diagnosis(SWPSO-BPN) model based on combining improved particle swarm optimization (PSO) algorithm and Back-propagation(BP) neural network is proposed in this paper. In the SWPSO-BPN method, an improved PSO (SWPSO) algorithm is proposed to optimize the parameters of BP neural network in order to overcome the shortcomings of slow learning speed and being easy to fall into local minimum, and obtain the optimal values of parameter combination in the BP neural network(SWPSO-BPN) model. Then proposed SWPSO-BPN model is applied to diagnose the fault in order to obtain a new fault diagnosis (SWPSO-BPN-FD) method. Finally, the proposed SWPSO-BPN-FD method is used to test the data from bearing data center of CWRU. The experimental results show that the proposed SWPSO-BPN-FD method can accurately and effectively realize high precision fault diagnosis of rolling bearing. And this method takes on strong robustness and generalization ability.

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