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基于ResNeXt-50的四质振动MEMS陀螺仪的故障诊断方法,具有注意力机制和改进的EWT算法

Yikuan Gu^1,2, Yan Wang³, Zhong Li^1,2

¹School of Software, North University of China, Taiyuan 030051, China.

|July 29, 2023

概括

这项研究引入了一种用于四质振动MEMS陀螺仪 (FMVMG) 的新型故障识别算法. 将增强的实证波形变换 (EWT) 与SeresNeXt-50相结合,通过减少噪音,显著提高了故障检测的准确性.

科学领域:

信号处理信号处理
机器学习机器学习
在 MEMS 技术方面,MEMS 技术

背景情况:

在MEMS陀螺仪中准确的故障识别对于可靠的系统性能至关重要.
现有的方法可能会与噪声和复杂的信号特征作斗争.

研究的目的:

为四质振动MEMS陀螺仪 (FMVMG) 开发一个先进的故障识别算法.
加强信号处理技术,以改善降噪和特征提取.

主要方法:

使用四质振动MEMS陀螺仪 (FMVMG) 来获取信号.
采用了改进的实证波形变换 (EWT) 算法,结合多尺度顺序来进行信号分解和噪声分离.
将1D信号重建为2D图像信号,以增强功能提取.
应用了SeresNeXt-50深度学习模型与Squeeze-and-Excitation (SE) 模块用于故障分类.

主要成果:

拟议的算法显著降低了白噪声对故障识别的影响.
与传统方法相比,在故障识别准确度方面取得了显著的改进.
与BP神经网络相比,显示了30.25%的精度增加,与ResNeXt-50.5相比,显示了1.85%的精度增加.

结论:

完善的EWT和SeresNeXt-50的集成为FMVMG故障识别提供了一个强大的解决方案.
该方法有效地处理噪声,并提取复杂的信号特征,从而获得更高的准确性.
这种方法验证了先进的信号处理和机器学习用于陀螺仪故障诊断的有效性.

关键词:

问题问题问题问题这就是SENet的意义.深度神经网络是一个神经网络.消毒算法消毒算法错误诊断错误诊断错误诊断是一个问题.四大质量振动MEMS陀螺仪,四大质量振动的陀螺仪.

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