结合CNN和LSTM的滚动轴承剩余使用寿命预测方法  

Method of Predicting Remaining Useful Life of Rolling Bearing Combining CNN and LSTM

作　　者：王玉静[1] 李少鹏 康守强[1] 谢金宝[1] MIKULOVICH V I WANG Yujing;LI Shaopeng;KANG Shouqiang;XIE Jinbao;MIKULOVICH V I(School of Electrical and Electronic Engineering,Harbin University of cience and Technology Harbin,150080,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,哈尔滨150080 [2]白俄罗斯国立大学,明斯克220030

出　　处：《振动．测试与诊断》2021年第3期439-446,617,共9页Journal of Vibration,Measurement & Diagnosis

基　　金：国家自然科学基金资助项目(51805120);黑龙江省自然科学基金资助项目(LH2019E058);黑龙江省本科高校青年创新人才培养计划资助项目(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)

摘　　要：针对滚动轴承存在性能退化渐变故障和突发故障两种模式下的剩余使用寿命(remaining useful life,简称RUL)预测困难的问题,提出一种结合卷积神经网络(convolution neural networks,简称CNN)和长短时记忆(long short term memory,简称LSTM)神经网络的滚动轴承RUL预测方法。首先,对滚动轴承原始振动信号作快速傅里叶变换(fast Fourier transform,简称FFT);其次,将预处理所得到的频域幅值信号进行归一化处理后,将其作为CNN的输入,并利用CNN自适应提取局部内在有用信息,学习并挖掘深层特征,避免传统算法需要专家大量经验的弊端;然后,再将深层特征输入到LSTM网络中,构建趋势性量化健康指标,同时确定失效阈值;最后,运用移动平均法进行平滑处理,消除局部振荡,再利用多项式曲线拟合,预测未来失效时刻,实现滚动轴承RUL预测。实验结果表明,所提方法构建的趋势性量化健康指标在两种故障模式下都具有良好的单调趋势性,预测结果能够较好地接近真实寿命值。Aiming at the difficulty of predicting the remaining useful life(RUL)of a rolling bearing under the two modes of performance degradation gradual failure and sudden failure,a RUL prediction method is proposed combining convolution neural networks(CNN)with long short term memory(LSTM)neural networks.Firstly,the original vibration signal of the rolling bearing is transformed by fast Fourier transform(FFT),and then the frequency domain amplitude signal obtained by pre-processing is normalized as the input of CNN.CNN can adaptively extract local intrinsic useful information,learn and excavate deep features,thus avoiding the drawbacks of traditional algorithms that require a lot of experts'experience.Then deep features are input into LSTM neural networks to construct trend quantification health indicators to determine the failure threshold.Finally,the moving average method is used to smooth out the local oscillation,and then the polynomial curve fitting is used to predict the future failure time and realize the RUL prediction of rolling bearings.The experimental results show that,under the two modes,the trend quantification health indicators constructed by the proposed method all have good monotonous trend and prediction results,which can better approach the real life values.

关 键 词：滚动轴承 卷积神经网络 长短时记忆神经网络 趋势性量化健康指标 剩余使用寿命预测 

分 类 号：TH133.33[机械工程—机械制造及自动化] TN911.7[电子电信—通信与信息系统]