文献类型：期刊文献

英文题名：Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition

作者：Liu, Shiyuan[1,2];Yu, Xiao[3];Qian, Xu[2];Dong, Fei[3]

第一作者：Liu, Shiyuan

通讯作者：Qian, X[1]

机构：[1]Beijing Union Univ, Coll Appl Sci & Technol, Beijing 100083, Peoples R China;[2]China Univ Min & Technol Beijing, Sch Mech Elect & Informat Engn, Beijing 100083, Peoples R China;[3]China Univ Min & Technol, IOT Percept Mine Res Ctr, Xuzhou 221000, Jiangsu, Peoples R China

第一机构：北京联合大学应用科技学院

通讯机构：[1]corresponding author), China Univ Min & Technol Beijing, Sch Mech Elect & Informat Engn, Beijing 100083, Peoples R China.

年份：2020

卷号：2020

外文期刊名：SHOCK AND VIBRATION

收录：;EI(收录号：20204109320332);Scopus(收录号：2-s2.0-85092091925);WOS:【SCI-EXPANDED(收录号:WOS:000572349200001)】；

基金：This work was funded by the Special Funds Project for Transforming Scientific and Technological Achievements in Jiangsu Province (BA2016017); the National Key R&D Program of China (2017YFC0804400 and 2017YFC0804401).

语种：英文

外文关键词：Dimensionality reduction - Failure analysis - Feature extraction - Learning systems - Metal drawing - Roller bearings - Signal processing - Transfer learning - Vibration analysis

摘要：In real industrial scenarios, the working conditions of bearings are variable, and it is therefore difficult for data-driven diagnosis methods based on conventional machine-learning techniques to guarantee the desirable performance of diagnosis models, as the models assume that the distributions of both the training and testing data are the same. To enhance the performance of the fault diagnosis of bearings under different working conditions, a novel diagnosis framework inspired by feature extraction, transfer learning (TL), and feature dimensionality reduction is proposed in this work, and dual-tree complex wavelet packet transform (DTCWPT) is used for signal processing. Additionally, transferable sensitive feature selection by ReliefF and the sum of mean deviation (TSFSR) is proposed to reduce the redundant information of the original feature set, to select sensitive features for fault diagnosis, and to reduce the difference between the marginal distributions of the training and testing feature sets. Furthermore, a modified feature reduction method, the local maximum margin criterion (LMMC), is proposed to acquire low-dimensional mapping for high-dimensional feature spaces. Finally, bearing vibration signals collected from two test rigs are analyzed to demonstrate the adaptability, effectiveness, and practicability of the proposed diagnosis framework. The experimental results show that the proposed method can achieve high diagnosis accuracy and has significant potential benefits in industrial applications.