自动化学报2023，Vol.49Issue(12)：2627-2638,12.DOI:10.16383/j.aas.c200890
基于无监督深度模型迁移的滚动轴承寿命预测方法
Rolling Bearing Life Prediction Based on Unsupervised Deep Model Transfer
摘要
Abstract
In order to solve the problems such as difficulty in acquiring labeled vibration data of rolling bearings under certain working condition in practice,difficulty in constructing health indicators and large error in life predic-tion of rolling bearings,a method of remaining useful life(RUL)prediction of rolling bearings is proposed based on unsupervised deep model transfer.Firstly,the root mean square(RMS)features of the vibration data of the full life cycle of the rolling bearings are extracted,and a new bottom-up(BUP)time series segmentation algorithm is intro-duced to divide the feature sequence into three states:Normal period,degradation period and recession period.Mark the state information of the amplitude sequence of the vibration signal after the fast Fourier transform(FFT),and input it into the fully convolutional neural network(FCN)of the newly added convolutional layer to extract deep features,and the pre-trained model can be obtained.The gradient of the pre-trained model is proposed and used as a"feature"to participate in the target domain network training process together with the traditional pre-trained model features,and the state identification model is obtained.Using state probability estimation method combined with state identification model,life prediction model of rolling bearing can be established.Experiments verify that,without establishing health indicators,the proposed method can realize remaining useful life prediction of rolling bearings for different working conditions under unsupervised conditions,and achieve better results.关键词
滚动轴承/不同工况/模型迁移/状态识别/剩余使用寿命Key words
Rolling bearing/different working conditions/model transfer/state identification/remaining useful life(RUL)引用本文复制引用
康守强,邢颖怡,王玉静,王庆岩,谢金宝,MIKULOVICH Vladimir Ivanovich..基于无监督深度模型迁移的滚动轴承寿命预测方法[J].自动化学报,2023,49(12):2627-2638,12.基金项目
国家自然科学基金(52375533),山东省自然科学基金(ZR2023 ME057)资助Supported by National Natural Science Foundation of China(52375533)and Natural Science Foundation of Shandong Pro-vince(ZR2023ME057) （52375533）
