摘要
工业大数据具有多类型、多维度的特点,单一类型的深度学习网络结构无法充分提取数据中包含的性能退化特征。针对上述问题,提出一种可同时融合处理一维时间序列数据和二维图像数据的多输入混合深度学习网络健康因子构建模型。根据输入数据类型特点搭建的混合深度学习网络包含时间特征提取层、空间特征提取层、融合层和全连接层。时间特征提取层主要由叠加的多个长短时记忆(long short-term memory,LSTM)网络构成,用于提取一维时间序列数据中蕴含的时间特征。空间特征提取层主要由深度卷积神经网络(deep convolutional neural network,DCNN)构成,用于提取二维图像数据中的空间特征。融合层将时间特征与空间特征融合。最后,利用全连接层输出健康因子值。滚动轴承全寿命周期试验结果表明:本文提出的多输入混合深度学习网络的健康因子构建方法能够深度挖掘不同数据类型包含的性能退化信息,有效降低了性能退化曲线的离散性,有助于减小剩余寿命预测结果的不确定性,同时在一定程度上提高了单调性和趋势性,提高了剩余寿命预测精度。
Due to the multi-type and multi-dimension characteristics of the industrial big data,the single deep learning network can not extract the performance degradation characteristics effectively from the data.To solve the problem,the health indicator construction model based on the multi-input hybrid deep learning network is proposed,which can fuse and process the one-dimensional time series data and two-dimensional pictorial data simultaneously.According to the characteristic of the input data,the constructed hybrid deep learning network mainly is consisted of the time features extraction layer,spatial features extraction layer,fusion layer and fully connected layer.The time features extraction layer is constructed by stacking the multiple long short-term memory(LSTM)network to extract temporal features from the one-dimensional time series data.The spatial features extraction layer is constructed by deep convolutional neural network(DCNN)which is used to extract the spatial features from the two-dimensional pictorial data.The fusion layer is designed to fuse the time features and the spatial features,and the fully connected layer output the health indicator which could reflect the performance degradation process.The rolling element bearing life-cycle experiment indicates that the health indicator construction method based on the multi-input hybrid deep learning network could extracts and fuses different data type and reduces the discreteness of the degradation curve,which reduces the uncertainty of the remaining useful life prediction results.What is more,the proposed method could promote the monotonicity and trendability.The remaining useful life prediction result shows that the proposed health indicator could promote the remaining useful life prediction accuracy effectively.
作者
孙世岩
张钢
田福庆
梁伟阁
SUN Shiyan;ZHANG Gang;TIAN Fuqing;LIANG Weige(College of Weaponry Engineering, Naval University of Engineering, Wuhan 430033, China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2020年第10期2390-2398,共9页
Systems Engineering and Electronics
基金
国家自然科学基金(61640308)
海军工程大学自然科学基金(20161579)资助课题。
关键词
工业大数据
深度学习
健康因子
评估准则
离散性
industrial big data
deep learning
health indicator
evaluation criterion
discreteness
作者简介
通信作者:孙世岩(1979-),男,副教授,硕士研究生导师,博士,主要研究方向为决策分析与武器系统优化,E-mail:ssy9751119@163.com;张钢(1992-),男,博士研究生,主要研究方向为机械设备智能监测与故障预测,E-mail:782045624@qq.com;田福庆(1962-),男,教授,博士研究生导师,博士,主要研究方向为机械设备故障诊断与故障预测技术,E-mail:tianfq001@126.com;梁伟阁(1985-),男,讲师,博士,主要研究方向为机械设备故障诊断,E-mail:Lwinger@outlook.com。
