基于Alexnet-Adaboost的多工况滚动轴承故障识别方法

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振动与冲击 ›› 2022, Vol. 41 ›› Issue (2) : 20-25.

论文

唐贵基，田寅初，田甜

作者信息 +

Multi-working condition rolling bearing fault identification method based on the AlexNet-Adaboost algorithm

TANG Guiji，TIAN Yinchu，TIAN Tian

Author information +

文章历史 +

摘要

针对实际工程中滚动轴承多工况下传统故障诊断方法识别率偏低的情况。论文提出了一种基于Alexnet-Adaboost相结合的滚动轴承故障识别方法。以滚动轴承信号的时频图作为模型输入，分类结果为模型输出，训练多个Alexnet基分类器；在此基础上利用Adaboost（自适应提升）算法进一步提升得到强分类器，将多工况下滚动轴承信号的时频图输入强分类器进行测试。结果显示，所提方法可实现对多工况下滚动轴承故障有效识别，并且在一定程度上提高了故障分类的准确性。

Abstract

In view of the fact that the recognition rate of the traditional fault diagnosis method of rolling bearing is low under multiple working conditions in practical engineering. In this paper, a rolling bearing fault identification method based on Alexnet-Adaboost was proposed. Taking the time-frequency diagram of the rolling bearing signal as the model input and the classification result as the model output, several Alexnet-based classifiers were trained; on this basis, the Adaboost (adaptive lifting) algorithm was used to further improve the strong classifier, and the time-frequency diagram of the rolling bearing signal under multiple working conditions was input into the strong classifier for testing. The results show that the proposed method can effectively identify rolling bearing faults under multiple working conditions, and improves the fault classification accuracy to a certain extent.

导出引用

唐贵基，田寅初，田甜. 基于Alexnet-Adaboost的多工况滚动轴承故障识别方法[J]. 振动与冲击, 2022, 41(2): 20-25

TANG Guiji，TIAN Yinchu，TIAN Tian. Multi-working condition rolling bearing fault identification method based on the AlexNet-Adaboost algorithm[J]. Journal of Vibration and Shock, 2022, 41(2): 20-25

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