基于改进SAE和双向LSTM的滚动轴承RUL预测方法  

RUL Prediction Method of a Rolling Bearing Based on Improved SAE and Bi-LSTM

作　　者：康守强[1] 周月 王玉静[1] 谢金宝[1] MIKULOVICH Vladimir Ivanovich KANG Shou-Qiang;ZHOU Yue;WANG Yu-Jing;XIE Jin-Bao;MIKULOVICH Vladimir Ivanovich(College of Electrical and Electronic Engineering,Harbin University of Science and Technology,Harbin 150000,China;Belarusian State University,Minsk 220030,Belarus)

机构地区：[1]哈尔滨理工大学电气与电子工程学院,中国哈尔滨150000 [2]白俄罗斯国立大学,白俄罗斯明斯克220030

出　　处：《自动化学报》2022年第9期2327-2336,共10页Acta Automatica Sinica

基　　金：国家自然科学基金(51805120);黑龙江省自然科学基金(LH-2019E058);黑龙江省本科高校青年创新人才培养计划(UNPYSCT-2017091);黑龙江省普通高校基本科研业务专项资金资助项目(LGYC2018JC022)资助。

摘　　要：针对稀疏自动编码器(Sparse auto encoder,SAE)采用sigmoid激活函数容易造成梯度消失的问题,用一种新的Tan函数替代原有的sigmoid函数;针对SAE采用Kullback-Leibler(KL)散度进行稀疏性约束在回归预测方面的局限性,以dropout机制替代KL散度实现网络的稀疏性.利用改进SAE对滚动轴承振动信号进行无监督深层特征自适应提取,无需人工设计标签进行有监督微调.同时,考虑到滚动轴承剩余使用寿命(Remaining useful life,RUL)预测方法一般仅考虑过去信息而忽略未来信息,引入双向长短时记忆网络(Bi-directional long short-term memory,Bi-LSTM)构建滚动轴承RUL的预测模型.在2个轴承数据集上的实验结果均表明,所提基于改进SAE和Bi-LSTM的滚动轴承RUL预测方法不仅可以提高模型的收敛速度而且具有较低的预测误差.Since the sigmoid activation function of sparse auto-encoder(SAE)is easy to cause the gradient to disappear,a new Tan function is used to replace the original sigmoid function.In SAE,for the limitations in regression prediction when Kullback-Leibler(KL)divergence is used for sparseness constraints,KL divergence is replaced with the dropout mechanism to achieve network sparsity.And the improved SAE is used to perform unsupervised adaptive deep feature extraction for the vibration signals of rolling bearings,without designing labels manually for supervised fine adjustment.Meanwhile,for the remaining useful life(RUL)prediction method of rolling bearing,generally only the past information is considered and the future information is ignored,the bi-directional long short-term memory(Bi-LSTM)is introduced to construct an RUL prediction model of the rolling bearing.Using two bearing data sets,experimental results both show that the proposed RUL prediction method of a rolling bearing based on improved sparse auto encoder and Bi-LSTM can improve the convergence speed of the model and has lower prediction error.

关 键 词：滚动轴承 稀疏自动编码器 无监督特征提取 双向长短时记忆网络 剩余使用寿命预测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TH133.33[自动化与计算机技术—控制科学与工程]