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一个智能球轴承故障诊断系统,在光谱图上使用增强的旋转特性

Gyujin Seong¹, Dongwan Kim¹

¹Department of Electronics Engineering, Dong-A University, Busan 49315, Republic of Korea.

Sensors (Basel, Switzerland)

|February 10, 2024

概括

这项研究引入了一种新的球轴承故障诊断系统,使用旋转特征强调 (RCE) 谱图和优化的CNN. 该系统实现了高精度与低复杂性,适合不同的旋转频率.

科学领域:

机械工程机械工程
信号处理信号处理
人工智能的人工智能

背景情况:

球轴承的故障是旋转机械故障的主要原因.
现有的故障诊断方法,如温度和声音监测,由于外部噪声而存在局限性.
振动监测,特别是使用 mel-frequency cepstral 系数 (MFCC) 和 2D 卷积神经网络 (CNN),是一个有前途的替代方案,但面临着高采样率,广泛频率利用和模型复杂性的挑战.

研究的目的:

解决现有的基于振动的轴承故障诊断方法的局限性.
提出一个新的故障诊断系统,它是准确的,计算效率高,并且可以适应不断变化的操作条件.
为了减少轴承故障检测系统的复杂性和资源需求.

主要方法:

开发一个旋转特征强调 (RCE) 谱图过程,用于分析狭窄的频段并生成低分辨率图像.
优化,浅结构卷积神经网络 (CNN) 的设计,以实现高效的故障分类.
使用实验数据对拟议系统的验证.

主要成果:

拟议的系统实现了0.9974.4的高诊断准确度.
优化的CNN模型表现出低复杂度,只有5.81KB的参数和1.53×10^6的FLOP.
该系统有效地处理低采样率和不同的旋转频率.

结论:

拟议的RCE光谱图和优化的CNN为球轴承故障诊断提供了高度准确和低复杂性的解决方案.
这种方法克服了传统的MFCC和复杂的2D CNN方法的局限性.
开发的系统适用于需要可适应和高效的机械健康监测的现实应用.

关键词:

卷积神经网络是一种卷积神经网络.故障诊断系统系统故障诊断系统过器银行过器银行信号处理信号处理信号处理

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