Diagnostic Strategy Optimization Based on Particle Swarm Algorithm

Yan Sheng Zhang; Zhong Tao Qiao; Jian Hui Jing

doi:10.4028/www.scientific.net/AMM.215-216.555

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 215-216Diagnostic Strategy Optimization Based on Particle...

Diagnostic Strategy Optimization Based on Particle Swarm Algorithm

Abstract:

Fault diagnostic strategy profoundly influences diagnostic efficiency and cost. Diagnostic strategy optimization is a Non-Polynomial optimizing problem, which is a hard one. Applying conventional methods to resolving it, there are some difficulties: more complex to implementing algorithm, more time to diagnosing and more difficult to attaining global optimum. Particle swarm algorithm (PSA) is a new intelligent optimization algorithm, and applied to optimizing diagnostic strategy. Function of all-in cost is constructed by state probability, isolating matrix and test cost, serving as objective function. Test sequences are directly put into particle codes. Particle speeds are transformed to learning probability towards the best one in the swarm. Given proper parameters in PSA, the method can search the global optimum in a little time. At last, an example shows the approach is feasible and effective.

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Periodical:

Applied Mechanics and Materials (Volumes 215-216)

Pages:

555-560

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.215-216.555

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Online since:

November 2012

Authors:

Yan Sheng Zhang, Zhong Tao Qiao, Jian Hui Jing

Keywords:

Design for Testability, Diagnostic Strategy, Particle Swarm Algorithm, Test Sequence

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