A classification method for rotor imbalance fault with ISFLA-SVM

Lei You; Qiyi Han; Ying Liang; Jin Wang

doi:10.1117/12.2574445

24 June 2020 A classification method for rotor imbalance fault with ISFLA-SVM

Lei You, Qiyi Han, Ying Liang, Jin Wang

Proceedings Volume 11526, Fifth International Workshop on Pattern Recognition; 115260B (2020) https://doi.org/10.1117/12.2574445
Event: Fifth International Workshop on Pattern Recognition, 2020, Chengdu, China

Abstract

In this paper, a classification method for rotor imbalance fault (RIF) using support vector machine (SVM) is proposed. It adopts an improved shuffled frog-leaping algorithm (ISFLA) to optimize the parameters of SVM. Given the nonuniformity and the defect of trapping into the local optimum solution of the initial population existed in SFLA, some improvement methods are presented in ISFLA-SVM. ISFLA employs random uniform design (RUD) to generate an initial population. Besides, the global optimum solution of the proposed method could be found by changing the updating strategy of X_w in the subgroup. The performance of these three classification algorithms, i.e., particle swarm optimization (PSO)-SVM, SFLA-SVM, and ISFLA-SVM are compared. Analysis results show that ISFLA-SVM has the highest recognition accuracy.

Citation Download Citation

Lei You, Qiyi Han, Ying Liang, and Jin Wang "A classification method for rotor imbalance fault with ISFLA-SVM", Proc. SPIE 11526, Fifth International Workshop on Pattern Recognition, 115260B (24 June 2020); https://doi.org/10.1117/12.2574445

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