基于麻雀算法优化宽度学习系统的轴承故障诊断

doi:10.3969/j.issn.1007-7375.2023.03.017

工业工程 ›› 2023, Vol. 26 ›› Issue (3): 151-158.doi: 10.3969/j.issn.1007-7375.2023.03.017

基于麻雀算法优化宽度学习系统的轴承故障诊断

陈光林¹, 于丽娅¹, 张成龙², 周鹏¹, 李笑瑜³

1. 贵州大学机械工程学院，贵州贵阳 550025;
2. 中国矿业大学计算机科学与技术学院，江苏徐州 221116;
3. 贵州大学计算机科学与技术学院，贵州贵阳 550025

收稿日期:2022-03-23 发布日期:2023-07-08
作者简介:陈光林(1999-),男,贵州省人,硕士研究生,主要研究方向为人工智能、机器学习、群智能优化算法
基金资助:
国家重点研发计划资助项目(2020YFB1713300)

Bearing Fault Diagnosis Using Sparrow Algorithm to Optimize Broad Learning Systems

CHEN Guanglin¹, YU Liya¹, ZHANG Chenglong², ZHOU Peng¹, LI Xiaoyu³

1. School of Mechanical Engineering, Guizhou University, Guiyang 550025, China;
2. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China;
3. School of Computer Science and Technology, Guizhou University, Guiyang 550025, China

Received:2022-03-23 Published:2023-07-08

摘要/Abstract

摘要： 对滚动轴承做健康监测和故障诊断可以保证机械设备持续有效地进行工作。利用深度学习对工业大数据背景下的海量复杂数据进行建模时，需要耗费大量的计算资源，出现训练停滞或难以训练等问题。尝试采用宽度学习系统代替深度学习进行轴承故障诊断，同时针对宽度学习系统分类效果受限于自身超参数的选择这一问题，利用元启发算法中的麻雀搜索算法对宽度学习系统的超参数进行优化，提高宽度学习系统的准确率。将优化后的模型应用于西储大学轴承数据集，并与多种神经网络模型进行对比以验证所提方法的故障诊断能力。

关键词: 滚动轴承, 故障诊断, 麻雀搜索算法, 宽度学习系统

Abstract: Health monitoring and fault diagnosis of rolling bearings can ensure continuous and effective work of mechanical equipment. When using deep learning to model the massive and complex data in the context of industrial big data, it needs a lot of computational resources, resulting in problems such as training stagnation or difficulty in training. This paper attempts to use Broad Learning Systems to replace deep learning for bearing fault diagnosis. To address the problem that the classification effect of a broad learning system is limited by the choice of its own hyperparameters, the sparrow search algorithm in metaheuristic algorithms is used to optimize the hyperparameters of a Broad Learning System to improve the accuracy of the broad learning system. The optimized model is applied to the bearing dataset from Western Reserve University and compared with various neural network models to verify the fault diagnosis capability of the proposed method.

Key words: rolling bearing, fault diagnosis, sparrow search algorithm (SSA), broad learning system

中图分类号:

TP18
TH133.3

陈光林, 于丽娅, 张成龙, 周鹏, 李笑瑜. 基于麻雀算法优化宽度学习系统的轴承故障诊断[J]. 工业工程, 2023, 26(3): 151-158.

CHEN Guanglin, YU Liya, ZHANG Chenglong, ZHOU Peng, LI Xiaoyu. Bearing Fault Diagnosis Using Sparrow Algorithm to Optimize Broad Learning Systems[J]. Industrial Engineering Journal, 2023, 26(3): 151-158.

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基于麻雀算法优化宽度学习系统的轴承故障诊断

Bearing Fault Diagnosis Using Sparrow Algorithm to Optimize Broad Learning Systems

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