基于LSTM的工业互联网设备工作状态预测

LSTM-based Working State Prediction of Industrial Internet Equipment

随着工业互联网技术的发展,工业互联网设备的工作状态预测对于提升设备的可靠性具有重要意义。在实际的工业场景中,由于设备数据高度离散且在多个时间段内相互重合,简单的单信号预测和阈值方法是无效的。本文提出一种基于LSTM(长短时记忆)神经网络的设备工作状态预测模型。首先使用SMOTE算法进行数据倾斜处理,利用PCA算法进行数据降维,之后基于LSTM神经网络构建设备工作状态预测模型,最后利用F1分数值进行模型评估。本文基于真实的空调压缩机数据进行实验验证,实验结果表明了本文方法的有效性。

With the development of industrial Internet technology,working state prediction of industrial Internet equipment is of great significance for improving the reliability of equipment. In practical industrial scenarios,simple single-signal prediction and threshold methods are ineffective because the data is highly discrete and coincides over multiple time periods. This paper presents a working state prediction model of industrial Internet equipment based on LSTM( Long Short-Term Memory) neural network.Firstly,this paper uses the SMOTE algorithm for data skew processing and the PCA algorithm for data dimensionality reduction,then builds the working state prediction model of industrial Internet equipment based on LSTM neural network. Finally the model is evaluated by F1-Score. This paper is based on real air conditioning compressor data for experimental verification. The experimental results show the effectiveness of the proposed method.