基于深度融合神经网络的轴承健康指标构建  

Deep fusion neural network for health indicator construction of bearings

作　　者：岳研 刘畅[1,2] 刘韬[1,2] Yue Yan;Liu Chang;Liu Tao(Faculty of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming 650504,China;Key Laboratory Advanced Equipment Intelligent Manufacturing Technology,Yunnan Province,Kunming University of Science and Technology,Kunming 650504,China)

机构地区：[1]昆明理工大学机电工程学院,昆明650504 [2]昆明理工大学云南省先进装备智能制造技术重点实验室,昆明650504

出　　处：《电子测量与仪器学报》2021年第7期44-52,共9页Journal of Electronic Measurement and Instrumentation

基　　金：国家自然科学基金(51875272);昆明理工大学引进人才科研启动基金(KK23201801048)项目资助。

摘　　要：基于深度学习方法构建轴承健康指标正成为机械故障诊断领域新的研究内容和应用热点。基于深度学习的指标构建容易受到前期人工特征提取和特征选择的影响,且缺乏对多通道传感器信息进行有效融合。针对上述问题,设计了一种基于深度融合神经网络(DFNN)的多通道信息融合健康指标构建方法。首先,提出一种多通道融合特征提取器(MFE)从传感器原始信号提取轴承退化特征,然后设计一种自适应特征选择器(AFS)进行特征选择,最后引入双向长短期记忆网络(BiLSTM)构建健康指标。所提出的方法在轴承全寿命数据集上进行实验验证,实验结果表明,相比现有基于深度学习的轴承健康指标构建方法,DFNN构建的健康指标趋势性指标高达98.4%,单调性指标增加44%,因而能够更准确的反映轴承实际性能退化趋势。Deep learning-based health indicator construction has become a new research and application hotspot in the field of machinery fault diagnostics. The performance of deep learning-based health indicators is largely depending on hand-craft feature extraction and selection. Moreover, the correlation of multi-channel sensor signals is not enough considered. In response to the above problems, a method for constructing health indicators based on multi-channel information fusion based on Deep Fusion Neural Network(DFNN) is designed. First, a multi-channel feature extractor(MFE) is proposed to extract bearing degradation features from the raw vibration signals. Then an adaptive feature selector(AFS) is designed to select useful features automatically. After MFE and AFS, we utilized a bidirectional long-short-term memory(BiLSTM) network to construct bearing health indicator. The proposed method is experimentally verified on the bearing life data set. The result shows that compared with some state-of-the art methods, the health indicator by DFNN is up to 98.4%, and the monotonic indicator increases by 44%. Therefore, it is able to map the bearing degradation process effectively.

关 键 词：深度学习 健康指标 滚动轴承 特征提取 

分 类 号：TH133.3[机械工程—机械制造及自动化] TN911.72[电子电信—通信与信息系统]