引用本文:陈志刚,杜小磊,王衍学,等.改进集成深层自编码器在轴承故障诊断中的应用[J].控制与决策,2021,36(1):135-142
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改进集成深层自编码器在轴承故障诊断中的应用
陈志刚1,2, 杜小磊1,2, 王衍学1,3, 张楠1
(1. 北京建筑大学机电与车辆工程学院,北京100044;北京市建筑安全监测工程技术研究中心,北京100044;北京市建筑安全监测工程技术研究中心,北京100044;3. 北京建筑大学城市轨道交通车辆服役性能保障北京市重点实验室,北京100044)
摘要:
针对滚动轴承振动信号故障特征难以自动提取和故障类别难以自动准确识别的问题,提出一种改进集成深层自编码器(IEDAE)方法.首先,改进自编码器的损失函数并设计3种小波卷积自编码器;其次,利用区分自编码器、小波卷积自编码器等5种自编码器构造相应的深层自编码器,并设计“跨层”连接以缓解深层网络的梯度消失现象,实现对轴承振动信号的无监督预训练和有监督微调;最后,通过加权平均法输出识别结果,以保证诊断结果的准确性和稳定性.实验结果表明,改进集成深层自编码器方法能有效地对滚动轴承进行多种工况和多种故障程度的识别,较好地摆脱了对人工特征提取的依赖,特征提取能力和识别能力优于现有其他方法.
关键词:  滚动轴承  故障诊断  深层自编码器  集成学习
DOI:10.13195/j.kzyjc.2019.0270
分类号:TH133.3
基金项目:国家自然科学基金项目(51605022,51875032);北京市优秀人才培养资助项目(2013D005017000013);北京市教育委员会科技计划一般项目(SQKM201710016014);北京市属高校基本科研业务费专项资金项目(X18217).
Application of improved ensemble deep auto-encoder in bearing fault diagnosis
CHEN Zhi-gang1,2,DU Xiao-lei1,2,WANG Yan-xue1,3,ZHANG Nan1
(1. School of Machine-electricity and Automobile Engineering,Beijing University of Civil Engineering and Architecture,Beijing100044,China;2. Beijing Engineering Research Center of Monitoring for Construction Safety,Beijing100044,China;3. Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles,Beijing University of Civil Engineering and Architecture,Beijing100044,China)
Abstract:
Considering that the difficulty of fault feature extraction of rolling bearing vibration signals and the difficulty of automatic and accurate identification of fault categories, a method based on improved ensemble deep auto-encoder(IEDAE) is proposed. Firstly, the loss function of the auto-encoder is improved and three kinds of wavelet convolution auto-encoders are designed. Then, five kinds of auto-encoders, such as discriminative auto-encoder and wavelet convolution auto-encoder, are employed to construct the corresponding deep auto-encoders, and a "cross-layer" connection is designed to alleviate the gradient disappearance of the deep network, and unsupervised pre-training and supervised fine-tuning of bearing vibration signals are realized. Finally, the recognition result is given using the weighted averaging method to ensure accurate and stable diagnosis result. The experimental results show that the IEDAE can effectively identify the bearing faults under multiple working conditions and multiple fault severities, which can get rid of the dependence on manual feature extraction and has better ability of feature extraction and recognition than other existing methods.
Key words:  rolling bearing  fault diagnosis  deep auto-encoder  ensemble learning

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