基于长短期记忆网络时序数据趋势预测及应用

Long Short-Term Memory Network Based Method and Its Application in Time-Series Data Trend Prediction

关键词:  主成分分析  长短期记忆网络  人工经验  时序数据  趋势预测

Key words:  Principal component analysis  Long short-term memory network  Artificial experience  Time series data  Trend prediction

摘要:

为了研究状态监测大数据对设备运行状态的估计和预测，提出了一种人工经验与主成分分析相结合的长短期记忆网络方法（AEPCA-LSTM），利用运行过程中的监测时序数据对设备运行趋势进行预测。通过基于人工经验的主要成分分析方法（AEPCA）从状态监测系统中提取与目标变量最相关的状态变量作为输入；利用长短期记忆网络（LSTM）对目标变量趋势变化进行预测，并考虑运行过程中新数据样本的持续产生，对模型进行定期更新，以提高模型的动态适应性。将所提出的方法应用于船舶副机系统的涡轮增压器转速预测中，结果表明，该方法相对于传统的PCA-LSTM和LSTM具有更小的预测平均误差0.18037，展现了其在时序数据趋势预测的优势。

Abstract:

In order to study the estimation and prediction of equipment operation state by condition monitoring big data， a novel method of Long-short Memory Network integrating Principal Component Analysis is proposed based on Artificial Experience （AEPCA-LSTM）， which uses the monitoring time series data during operation to predict the equipment health trends. Firstly， the Principal Component Analysis method based on Artificial Experience （AEPCA） is used to extract the state parameters most relevant to target variable from the state monitoring system as input. Secondly， the Long short-term Memory Network （LSTM） is used to predict the trend changes of the target variable considering the continuous generation of new data samples during operation， the model is regularly updated to improve the dynamic adaptability of the model. Finally， the proposed method is applied to the turbocharger speed prediction of marine auxiliary engine system. The results show that the method has a lower prediction loss of 0.18037 compared with PCA-LSTM and LSTM， which indicates its advantages in the prediction of trend in time series data.