郑州大学学报（理学版）2026，Vol.58Issue(3)：25-32,8.DOI:10.13705/j.issn.1671-6841.2024124
具有可解释性的DRWT-Trans轴承故障诊断方法
An Interpretable Bearing Fault Diagnosis Method Based on Adaptive Wavelet Denoising Network
摘要
Abstract
Deep learning-based bearing fault diagnosis methods often face challenges in extracting suffi-cient features from complex noisy operational data,and suffer from a lack of interpretability in their deci-sion-making processes.To address these challenges,an interpretable DRWT-Trans method was intro-duced for bearing fault diagnosis,to overcomes challenges in feature extraction from noisy operational data and to enhance decision-making transparency.The DRWT-Trans module emploe discrete wavelet decom-position with soft thresholdto for noise reduction and feature extraction.An interpretable module was de-signed for model analysis increase the interpretability of the model decision.The method's validity was tested on the Case Western Reserve University dataset and a factory gearbox dataset.关键词
轴承故障诊断/噪声/可解释/特征提取/深度学习Key words
bearing fault Diagnosis/noise/interpretability/feature extraction/deep learning分类
机械制造引用本文复制引用
刘晶,王梓玄,牛巍,季海鹏,武优西..具有可解释性的DRWT-Trans轴承故障诊断方法[J].郑州大学学报（理学版）,2026,58(3):25-32,8.基金项目
天津市制造业高质量发展专项资金项目(20232181) （20232181）
天津市科技计划项目(21JCZXJC00050) （21JCZXJC00050）
