Time series is a kind of data widely used in various fields such as electricity forecasting,exchange rate forecasting,and solar power generation forecasting,and therefore time series prediction is of great significanc...Time series is a kind of data widely used in various fields such as electricity forecasting,exchange rate forecasting,and solar power generation forecasting,and therefore time series prediction is of great significance.Recently,the encoder-decoder model combined with long short-term memory(LSTM)is widely used for multivariate time series prediction.However,the encoder can only encode information into fixed-length vectors,hence the performance of the model decreases rapidly as the length of the input sequence or output sequence increases.To solve this problem,we propose a combination model named AR_CLSTM based on the encoder_decoder structure and linear autoregression.The model uses a time step-based attention mechanism to enable the decoder to adaptively select past hidden states and extract useful information,and then uses convolution structure to learn the internal relationship between different dimensions of multivariate time series.In addition,AR_CLSTM combines the traditional linear autoregressive method to learn the linear relationship of the time series,so as to further reduce the error of time series prediction in the encoder_decoder structure and improve the multivariate time series Predictive effect.Experiments show that the AR_CLSTM model performs well in different time series predictions,and its root mean square error,mean square error,and average absolute error all decrease significantly.展开更多
针对强背景噪声下滚动轴承早期故障信号信噪比低、特征提取难度大的问题,提出一种将自回归-最小嫡解卷积(autoregressive-minimum entropy deconvolution,AR-MED)与Teager能量算子(teager energy operator,TEO)相结合的滚动轴承故障诊...针对强背景噪声下滚动轴承早期故障信号信噪比低、特征提取难度大的问题,提出一种将自回归-最小嫡解卷积(autoregressive-minimum entropy deconvolution,AR-MED)与Teager能量算子(teager energy operator,TEO)相结合的滚动轴承故障诊断方法。为了达到增强故障信号中冲击成分的目的,采用AR-MED对信号进行滤波处理。依据滤波后信号的Teager能量谱,获取滚动轴承的故障特征频率。通过对仿真信号和实测信号进行分析,验证了该文所提方法在强背景噪声下滚动轴承早期故障诊断中的有效性。展开更多
基金Shanxi Provincial Key Research and Development Program Project Fund(No.201703D111011)。
文摘Time series is a kind of data widely used in various fields such as electricity forecasting,exchange rate forecasting,and solar power generation forecasting,and therefore time series prediction is of great significance.Recently,the encoder-decoder model combined with long short-term memory(LSTM)is widely used for multivariate time series prediction.However,the encoder can only encode information into fixed-length vectors,hence the performance of the model decreases rapidly as the length of the input sequence or output sequence increases.To solve this problem,we propose a combination model named AR_CLSTM based on the encoder_decoder structure and linear autoregression.The model uses a time step-based attention mechanism to enable the decoder to adaptively select past hidden states and extract useful information,and then uses convolution structure to learn the internal relationship between different dimensions of multivariate time series.In addition,AR_CLSTM combines the traditional linear autoregressive method to learn the linear relationship of the time series,so as to further reduce the error of time series prediction in the encoder_decoder structure and improve the multivariate time series Predictive effect.Experiments show that the AR_CLSTM model performs well in different time series predictions,and its root mean square error,mean square error,and average absolute error all decrease significantly.
文摘针对强背景噪声下滚动轴承早期故障信号信噪比低、特征提取难度大的问题,提出一种将自回归-最小嫡解卷积(autoregressive-minimum entropy deconvolution,AR-MED)与Teager能量算子(teager energy operator,TEO)相结合的滚动轴承故障诊断方法。为了达到增强故障信号中冲击成分的目的,采用AR-MED对信号进行滤波处理。依据滤波后信号的Teager能量谱,获取滚动轴承的故障特征频率。通过对仿真信号和实测信号进行分析,验证了该文所提方法在强背景噪声下滚动轴承早期故障诊断中的有效性。
