机械科学与技术2025，Vol.44Issue(2)：288-297,10.DOI:10.13433/j.cnki.1003-8728.20230171
CNN-DLSTM结合迁移学习的小样本轴承故障诊断方法
CNN-DLSTM Combined with Transfer Learning for Small Sample Bearing Fault Diagnosis
摘要
Abstract
A bearing fault diagnosis method that connects a 1D convolutional neural network(1D-CNN)to a model of deep long short-term memory recurrent neural network(DLSTM)combined with transfer learning is proposed to address the problems of small fault data samples and difficulty in classifying unknown faults.The diagnostic method is based on motor vibration data and uses CNN to extract fault features;the features are used as input to the DLSTM,which further learns and encodes the feature sequence information learned from the CNN to capture high-level features for fault classification;the fault diagnosis model is first pre-trained with sufficient samples of Case Western Reserve University data,and then uses transfer learning to relax the training data and test data that do not need to be independently and identically distributed.The pre-training model was then fine-tuned using small samples of data from a home-made experimental platform.Finally,the transfer learning model is used to simulate experiments on faulty bearing data across operating conditions,models and faults.The results show that the proposed method is more robust and faster to train than other methods,and can diagnose faults more accurately,with an average diagnostic accuracy of over 99%.关键词
小样本数据集故障诊断/卷积神经网络/长短期记忆网络/迁移学习Key words
small sample data set fault diagnosis/convolutional neural network/long short-term memory network/transfer learning分类
机械制造引用本文复制引用
仇芝,徐泽瑜,陈涛,石明江,韦明辉..CNN-DLSTM结合迁移学习的小样本轴承故障诊断方法[J].机械科学与技术,2025,44(2):288-297,10.基金项目
国家自然科学基金项目(51804267)与四川省科技厅计划项目(2019YJ0318) （51804267）
