Application of an improved sparrow search algorithm in BP network classification of strip steel surface defect images

Ma, Guoyuan; Yue, Xiaofeng; Gao, Xiaoshuai; Liu, Fuqiuxuan

doi:10.1007/s11042-022-13757-4

Cited by 5 publications

(1 citation statement)

References 35 publications

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“…The Sparrow Search Algorithm (SSA) is a metaheuristic algorithm proposed in recent years [24], which is a meta-inspired algorithm inspired by sparrows searching for food and escaping from their pursuers. The sparrow search algorithm has many advantages such as simple implementation, less adjustment parameters required, high search accuracy, robustness, and stability [25,26], and is therefore of interest to a wide range of researchers. However, it also suffers from the problems of slow search speed, decreasing population diversity in the late stage of search, and easily falling into local optimum [27,28].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Network Based on Improved Sparrow Search Algorithm Optimization for Rolling Bearing Fault Diagnosis

Ma,

Yue,

Zhu

et al. 2023

Mathematics

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In recent years, deep learning has been increasingly used in fault diagnosis of rotating machinery. However, the actual acquisition of rolling bearing fault signals often contains ambient noise, making it difficult to determine the optimal values of the parameters. In this paper, a sparrow search algorithm (LSSA) based on backward learning of lens imaging and Gaussian Cauchy variation is proposed. The lens imaging reverse learning strategy enhances the traversal capability of the algorithm and allows for a better balance of algorithm exploration and development. Then, the performance of the proposed LSSA was tested on the benchmark function. Finally, LSSA is used to find the optimal modal component K and the optimal penalty factor α in VMD-GRU, which in turn realizes the fault diagnosis of rolling bearings. The experimental results show that the model can achieve a 96.61% accuracy in rolling bearing fault diagnosis, which proves the effectiveness of the method.

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