计算机科学
断层(地质)
方位(导航)
振动
学习迁移
人工智能
特征(语言学)
模式识别(心理学)
滚动轴承
特征向量
接头(建筑物)
工程类
结构工程
声学
地震学
哲学
地质学
物理
语言学
作者
Chenhui Qian,Quan Jiang,Yehu Shen,Chunran Huo,Qingkui Zhang
标识
DOI:10.1088/1361-6501/ac3b0b
摘要
Abstract Mechanical intelligent fault diagnosis is an important method to accurately identify the health status of mechanical equipment. Traditional fault diagnosis methods perform poorly in the diagnosis of rolling bearings under complex conditions. In this paper, a feature transfer learning model based on improved DenseNet and joint distribution adaptation (FT-IDJ) is proposed. With this model, we apply it to implement rolling bearing fault diagnosis. A lightweight DenseNet model is firstly proposed to extract the transferable features of the raw vibration signal. Furthermore, the parameters in the DenseNet are constrained by the domain adaptive regularization term and pseudo label learning. The marginal distribution discrepancy and the conditional distribution discrepancy of the learned transferable features are reduced by this way. The proposed method is validated by the diagnosis experiments with CWRU and Jiangnan University rolling bearing datasets. The experimental results showed that the proposed FT-IDJ can classify common rolling bearing faults such as inner ring fault, outer ring fault and rolling element fault. Also, it has higher classification accuracy than DAN and other eight methods, which demonstrates its effectively learning transferable features from auxiliary data.
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