A Deep Learning Fault Diagnosis Model with Strong Anti-Noise Ability

doi:10.7652/xjtuxb202104007

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A Deep Learning Fault Diagnosis Model with Strong Anti-Noise Ability

更新时间：2025-07-09

- A Deep Learning Fault Diagnosis Model with Strong Anti-Noise Ability
- Vol. 55, Issue 4, Pages: 61-68(2021)
- 作者机构：
  
  陆军装甲兵学院车辆工程系,北京,100072
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202104007
  CLC： TP206
- Online First：10 April 2021，
  
  Published：2021
- 稿件说明：
移动端阅览
A Deep Learning Fault Diagnosis Model with Strong Anti-Noise Ability[J]. 2021, 55(4): 61-68.
DOI：

A Deep Learning Fault Diagnosis Model with Strong Anti-Noise Ability[J]. 2021, 55(4): 61-68. DOI： 10.7652/xjtuxb202104007.

摘要

针对旋转机械运行环境噪声大

现有的基于深度学习的故障诊断模型抵抗强噪声干扰能力不足的问题

提出了一种具有强抗噪性的深度学习故障诊断模型(HAT-MSCNN)。首先将齿轮箱训练集正常状态下样本的频谱信号由大到小排列

取排序的第k个的幅值α为基准值

将训练集中所有样本的频谱信号低于α的值置0

降低了易与噪声耦合的低幅值频率成分对诊断模型的干扰

突出了高幅值频率成分对故障诊断模型的贡献

同时保留零值所在的位置信息。然后将经过此处理的训练集输入到由多种移动步长卷积核组合的多尺度卷积神经网络中进行训练

最终得到强抗噪性的故障诊断模型。以精确率和召回率的调和平均值F作为模型的评价指标

并使用加权梯度类激活映射方法可视化模型的分类权重

在具有丰富故障模式的PHM2009齿轮箱数据集上验证模型。将所提模型与以原始振动信号作为输入的Raw-MSCNN模型、以频谱信号作为输入的Frequency-MSCNN模型、以集合经验模态分解(EEMD)降噪后的信号作为输入的EEMD-MSCNN模型和以所提训练集样本构造方法处理后的信号作为输入的HAT-CNN模型进行对比

结果表明

所提模型在测试集信噪比为-4 dB时

诊断的F值为98.6%

比第二名的Frequency-MSCNN高了6.4%

在更强噪声干扰的测试集信噪比为-6 dB时

F值为89.1%

比Frequency-MSCNN高了10.9%

证明了所提的HAT-MSCNN模型具有在强噪声干扰下的抗噪能力。

Abstract

A deep learning fault diagnosis model with strong anti-noise ability(HAT-MSCNN)is proposed to solve the problem that the existing fault diagnosis model based on deep learning has insufficient resistance to strong noise interference due to the high noise in the operating environment of rotating machinery. Firstly

the frequency spectrum signals of the samples under normal state of the gearbox training set are arranged from large to small

and the kth amplitude value α of the sort is taken as the reference value. The frequency spectrum signals of all samples in the training set with values being lower than the value of α are set to zero

which reduces the interference of low-amplitude frequency components that are easy to be coupled with noise on the diagnosis model

and highlights the contribution of high-amplitude frequency components to the fault diagnosis model and retains the position information where the zero value is located. Then the processed training set is inputted into a multi-scale convolutional neural network composed of multiple moving step size convolution kernels for training

and finally a fault diagnosis model with strong noise immunity is obtained. The harmonic average value F of precisions and recalls is used as an evaluation index of the model

the weighted gradient activation mapping method is used to visualize the classification weights of the model

and the model is verified on the PHM2009 gearbox data set with rich failure modes. The proposed method is compared with the Raw-MSCNN

Frequency-MSCNN

and EEMD-MSCNN models that use vibration signals

frequency signals

and EEMD denoised signals as input respectively

and the HAT-CNN model that uses the signal processed by the proposed training set sample construction method as input. The results show that when the SNR of the test set is -4 dB

the F value of the proposed HAT-MSCNN is 98.6%

which is 6.4% higher than that of the second-place Frequency-MSCNN. When the SNR of the test set with stronger noise interference is -6 dB

the F value of the proposed HAT-MSCNN is 89.1%

which is 10.9% higher than that of Frequency-MSCNN. It proves that the proposed model has the anti-noise capability under strong noise interference.

关键词

Keywords

references

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