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

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关键词：可解释性；一维卷积神经网络；傅里叶变换；故障诊断；频域中图分类号：TH133.33文献标志码：ADOI：10.7652/xjtuxb202507003文章编号： 0253-987X（2025）07-0024-12

Study on the Interpretability of One-Dimensional Convolutional Neural Networksin Mechanical FaultFeatureExtraction

WANG Fangzhen1，ZHAGN Xiaoli1，ZHAO Qiwu1，WANG Baojian² （1.The Ministry of Education KeyLaboratory of Road Construction Technology and Equipment，Chang’an University， Xi'an 70o64，China；2.TheNational Demonstration Center for Mechanical Foundation Experimental Teaching， Xi'anJiaotongUniversity，Xi'an 71oo49，China）

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 beter accomplished. This study can provide new ideas and methods for revealing the“black box" mechanisms and interpretability of convolutional neural networks.

Keywords： interpretability;one-dimensional convolutional neural networks；Fourier transform; fault diagnosis；frequency domain

卷积神经网络（CNN）因其特征提取能力强、计算量小以及能够自动完成特征提取和故障模式分类的端到端诊断特点，已逐渐成为机械健康监测与智能维护的重要方法[1-2]。（剩余17278字）