A fault diagnosis method for fire control systems based on KPCA ISSA SVM

DOI:	10.11809/bqzbgcxb2024.08.004
Keywords:	fault diagnosis; fire control system; support vector machine; kernel principal component analysis; sparrow search algorithm
Abstract:	Traditional tank fault diagnosis mainly relies on expert experience, which requires a large amount of manpower and takes a long time. To meet the health management needs of armored equipment, a fault diagnosis method based on kernel principal component analysis and improved sparrow algorithm combined with support vector machine is proposed. In response to the problem of complex signal components and limited data volume in fire control systems, kernel principal component analysis is first used to reduce dimensionality and extract nonlinear features of fault data, reducing the impact of other redundant features on fault recognition and reducing data dimensions. Introducing chaotic Tent mapping and nonlinear inertia weighting factors to improve the sparrow search algorithm, optimizing the core parameters of support vector machine and establishing a fault diagnosis model, while conducting experimental comparisons with support vector machine models of particle swarm optimization and whale optimization. Experimental results have shown that this method can effectively diagnose faults in tank fire control systems, and has high performance in terms of accuracy and diagnostic efficiency.
Issue:	Vol. 45 No. 8 (2024)
Published:	2024-09-02
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