一维卷积神经网络在机械故障特征提取中的可解释性研究

王芳珍; 张小丽; 赵琦武; 王保建

doi:10.7652/xjtuxb202507003

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一维卷积神经网络在机械故障特征提取中的可解释性研究

专题　大数据智能故障诊断与预测 | 更新时间：2025-07-09

- 一维卷积神经网络在机械故障特征提取中的可解释性研究
- Study on the Interpretability of One-Dimensional Convolutional Neural Networks in Mechanical Fault Feature Extraction
- 西安交通大学学报 2025年59卷第7期页码：24-35
- 作者机构：
  
  1.长安大学道路施工技术与装备教育部重点实验室， 710064，西安
  2.西安交通大学机械基础国家级实验教学示范中心， 710049，西安
- 作者简介：
  
  王芳珍（1994—），男，硕士生；
  张小丽，女，副教授，博士生导师。
- 基金信息：
  
  陕西省自然科学基础研究计划资助项目(2021M-169;2023-JC-YB-477;2024GX-YBXM-265)
- DOI：10.7652/xjtuxb202507003
  中图分类号： TH133.33
- 收稿：2025-01-08，
  
  网络首发：2025-03-24，
  
  纸质出版：2025-07-10
- 稿件说明：
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王芳珍, 张小丽, 赵琦武, 等. 一维卷积神经网络在机械故障特征提取中的可解释性研究[J]. 西安交通大学学报, 2025,59(7):24-35.

WANG Fangzhen, ZHAGN Xiaoli, ZHAO Qiwu, et al. Study on the Interpretability of One-Dimensional Convolutional Neural Networks in Mechanical Fault Feature Extraction[J]. Journal of Xi’an Jiaotong University, 2025, 59(7): 24-35.
王芳珍, 张小丽, 赵琦武, 等. 一维卷积神经网络在机械故障特征提取中的可解释性研究[J]. 西安交通大学学报, 2025,59(7):24-35. DOI： 10.7652/xjtuxb202507003.

WANG Fangzhen, ZHAGN Xiaoli, ZHAO Qiwu, et al. Study on the Interpretability of One-Dimensional Convolutional Neural Networks in Mechanical Fault Feature Extraction[J]. Journal of Xi’an Jiaotong University, 2025, 59(7): 24-35. DOI： 10.7652/xjtuxb202507003.

摘要

针对一维卷积神经网络在机械故障诊断中的内部决策和推断过程未知，导致结果可解释性与可信度不足的问题，从信号特征提取的视角建立信号分析与神经网络之间的相似性联系，通过提取神经网络卷积层权重，观察信号时域、频域特征随网络层的变化规律，从而揭示神经网络特征提取的本质，并采用实验测试数据和凯斯西储大学轴承公开数据进行验证。结果表明：卷积核可以等效为有限脉冲滤波器，最大池化层能够满足简单二分类任务中神经网络的非线性化要求，此时的卷积层无需添加激活函数；神经网络能够通过逐层提高频率分辨率，寻找到接近理论故障特征频率的频率成分，此行为与傅里叶变换存在相似性；当频谱范围最终分解到1~3倍故障特征频率时，能够更好地完成识别任务。该研究可为揭示卷积神经网络的“黑盒”机制与可解释性提供新的思路与方法。

Abstract

To address the limited interpretability and reliability caused by the unknown internal decision-making and inference processes of one-dimensional convolutional neural networks (CNNs) in mechanical fault diagnosis

a similarity connection between signal analysis and neural networks is established from the perspective of feature extraction. By extracting the weights of the convolutional layers in the neural network and observing the variations in time/frequency domain features as the network layers change

this study reveals the intrinsic feature extraction behavior of neural networks. Experimental test data and publicly available bearing data from Case Western Reserve University are used for validation. The results indicate that the convolutional kernel can be equivalent to a finite impulse filter

and the max pooling layer can meet the non-linear requirements of neural networks for simple binary classification tasks

therefore not requiring an activation function in the convolutional layer; the neural network is capable of incrementally increasing frequency resolution layer by layer to identify frequency components close to theoretical fault characteristic frequencies

exhibiting similarities to Fourier transforms. When the spectral range is ultimately decomposed to 1 to 3 times the fault characteristic frequency

the identification task is better accomplished. This study can provide new ideas and methods for revealing the “black box” mechanisms and interpretability of convolutional neural networks.

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references

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