针对设备剩余使用寿命(RUL)预测过程中数据维度高,时间序列相关性信息难以充分考虑的实际应用需求,提出一种多尺度深度卷积神经网络和长短时记忆网络融合(multi-scale deep convolutional neural network and long short-term memory,MS...针对设备剩余使用寿命(RUL)预测过程中数据维度高,时间序列相关性信息难以充分考虑的实际应用需求,提出一种多尺度深度卷积神经网络和长短时记忆网络融合(multi-scale deep convolutional neural network and long short-term memory,MSDCNN-LSTM)的设备剩余寿命预测方法。对传感器数据进行标准化和滑动时间窗口处理得到输入样本;采用基于多尺度深度卷积神经网络(MSDCNN)提取空间详细特征,采用长短时记忆网络(LSTM)提取时间相关性特征以进行有效的预测。基于商用模块化航空推进系统仿真数据集的实验表明,相较于其他最新方法,文中提出的方法取得了较好的预测结果,尤其是对于故障模式和运行条件复杂的设备寿命预测需求,该方法效果明显。展开更多
To decrease breakdown time and improve machine operation reliability,accurate residual useful life(RUL) prediction has been playing a critical role in condition based monitoring.A data fusion method was proposed to ac...To decrease breakdown time and improve machine operation reliability,accurate residual useful life(RUL) prediction has been playing a critical role in condition based monitoring.A data fusion method was proposed to achieve online RUL prediction of slewing bearings,which consisted of a reliability based RUL prediction model and a data driven failure rate(FR) estimation model.Firstly,an RUL prediction model was developed based on modified Weibull distribution to build the relationship between RUL and FR.Secondly,principal component analysis(PCA) was introduced to process multi-dimensional life-cycle vibration signals,and continuous squared prediction error(CSPE) and its time-domain features were employed as equipment performance degradation features.Afterwards,an FR estimation model was established on basis of the degradation features and relevant FRs using simplified fuzzy adaptive resonance theory map(SFAM) neural network.Consequently,real-time FR of equipment can be obtained through FR estimation model,and then accurate RUL can be calculated through the RUL prediction model.Results of a slewing bearing life test show that CSPE is an effective indicator of performance degradation process of slewing bearings,and that by combining actual load condition and real-time monitored data,the calculation time is reduced by 87.3%and the accuracy is increased by 0.11%,which provides a potential for online RUL prediction of slewing bearings and other various machineries.展开更多
文摘针对设备剩余使用寿命(RUL)预测过程中数据维度高,时间序列相关性信息难以充分考虑的实际应用需求,提出一种多尺度深度卷积神经网络和长短时记忆网络融合(multi-scale deep convolutional neural network and long short-term memory,MSDCNN-LSTM)的设备剩余寿命预测方法。对传感器数据进行标准化和滑动时间窗口处理得到输入样本;采用基于多尺度深度卷积神经网络(MSDCNN)提取空间详细特征,采用长短时记忆网络(LSTM)提取时间相关性特征以进行有效的预测。基于商用模块化航空推进系统仿真数据集的实验表明,相较于其他最新方法,文中提出的方法取得了较好的预测结果,尤其是对于故障模式和运行条件复杂的设备寿命预测需求,该方法效果明显。
基金Projects(51375222,51175242)supported by the National Natural Science Foundation of China
文摘To decrease breakdown time and improve machine operation reliability,accurate residual useful life(RUL) prediction has been playing a critical role in condition based monitoring.A data fusion method was proposed to achieve online RUL prediction of slewing bearings,which consisted of a reliability based RUL prediction model and a data driven failure rate(FR) estimation model.Firstly,an RUL prediction model was developed based on modified Weibull distribution to build the relationship between RUL and FR.Secondly,principal component analysis(PCA) was introduced to process multi-dimensional life-cycle vibration signals,and continuous squared prediction error(CSPE) and its time-domain features were employed as equipment performance degradation features.Afterwards,an FR estimation model was established on basis of the degradation features and relevant FRs using simplified fuzzy adaptive resonance theory map(SFAM) neural network.Consequently,real-time FR of equipment can be obtained through FR estimation model,and then accurate RUL can be calculated through the RUL prediction model.Results of a slewing bearing life test show that CSPE is an effective indicator of performance degradation process of slewing bearings,and that by combining actual load condition and real-time monitored data,the calculation time is reduced by 87.3%and the accuracy is increased by 0.11%,which provides a potential for online RUL prediction of slewing bearings and other various machineries.
