基于S-PSO分类算法的故障诊断方法

doi:10.7527/S1000-6893.2015.0020

Abstract

Abstract:

On the basis of taking the known states of monitoring data as prior class labels, a novel supervised particle swarm optimization (S-PSO) classification algorithm is constructed and used in fault diagnosis. In order to improve the accuracy of fault diagnosis and reduce the influence of randomness on the classification algorithm, a novel intervention updating strategy named an adaptive detecting updating based on dynamic neighborhood (ADU-DN) is proposed to expand the particles' ability of searching the entire solution space and guide the particles' adaptively jumping out of the local optimal region, ensuring to obtain the global optimal solution. Meanwhile, a fitness function based on minimum distance of intra-class, maximum distance of inter-class and maximum classification precision of train samples is designed to make these three factors constraint the output optimal class centers, enhance classification algorithm's generality and fault-tolerant ability in fault diagnosis and increase the classification precision of test samples. The S-PSO classification algorithm overcomes the defects of the clustering algorithms that only consider the similarity features of data but not the physical meanings, and do not guide the classification of samples well. A comparison study on the GE90 engine borescope image texture feature classification is carried out and the research data show that S-PSO classification algorithm has a strong robustness and the classification precision is higher than the support vector machine (SVM) and the common neural network model.

Key words: S-PSO classification algorithm, dynamic neighborhood, adaptive detecting updating, fitness function, fault diagnosis

CLC Number:

V263.6
TP277

ZHENG Bo, GAO Feng. Fault diagnosis method based on S-PSO classification algorithm[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2015, 36(11): 3640-3651.

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Fault diagnosis method based on S-PSO classification algorithm

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