电子测量与仪器学报

车昱娇,陈云霞,崔宇轩.KPCA和改进LSTM在滚动轴承剩余寿命预测中的应用研究[J].电子测量与仪器学报,2021,35(2):109-114

KPCA和改进LSTM在滚动轴承剩余寿命预测中的应用研究

Rolling element bearing remaining useful life estimation based on KPCA and improved long short term memory network

DOI：

英文关键词:long short term memory network rolling element bearing dropout remaining useful life

基金项目:

作者	单位
车昱娇	1.北京航空航天大学北京100191
陈云霞	1.北京航空航天大学北京100191； 2.可靠性与环境工程技术国防科技重点实验室北京100191
崔宇轩	1.北京航空航天大学北京100191

Author	Institution
Che Yujiao	1Beihang University, Beijing 100191, China；
Chen Yunxia	1Beihang University, Beijing 100191, China；2Science and Technology on Reliability and Environmental Engineering Laboratory, Beijing 100191, China
Cui Yuxuan	1Beihang University, Beijing 100191, China；

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中文摘要:

提出了一种基于核主成分分析(KPCA)方法和运用了Dropout策略的长短时记忆神经网络(LSTM)的轴承剩余寿命预测方法。首先，提取了振动信号的有效值、最大值、峰峰值、峭度等14个时域特征指标。然后，利用KPCA方法融合轴承振动信号时域特征指标得到若干的主成分。提取若干主成分之中的第一主成分来评估研究对象的性能退化状态，利用主成分选取标准选择多个主成分作为本文预测模型的输入。接着建立利用Dropout策略进行改进的LSTM预测模型。最后，采用某轴承数据对本文所提方法进行了有效性验证。计算结果表明，对轴承退化程度的预测准确度达到了9592%，所提方法可以有效地来对轴承的剩余寿命进行预测，预测精度较高。

英文摘要:

This paper presents a method of bearing remaining useful life prediction based on KPCA and long short memory neural network (LSTM) with dropout. Firstly, 14 time domain indexes of vibration signal, such as effective value, maximum value, peak peak value and kurtosis, are extracted. KPCA method is used to fuse the time domain characteristic indexes of bearing vibration signal and extract the first principal component to evaluate the degradation of bearing performance, and multiple principal components that meet the requirements are used as the input of the prediction model. Then an improved LSTM prediction model based on dropout strategy is established finally, the bearing data are used to verify the proposed method. The results show that the proposed method can effectively predict the remaining useful life of the bearing, and has a good prediction effect. The prediction accuracy reaches 9592%.

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