基于并行LSTM-CNN的化工过程故障检测

Fault Detection of Chemical Process Based on Parallel LSTM-CNN

为保证生产过程的安全稳定运行,避免因故障导致损失,及时检测出异常工况并对异常工况进行准确诊断十分重要。针对化工过程的复杂性,提出一种并行长短时记忆网络和卷积神经网络(Parallel Long and Short-Term Memory Network and Convolutional Neural Network,PLSTM-CNN)模型进行化工生产过程故障检测。该模型有效结合LSTM对时间序列数据全局特征提取能力和CNN模型善于提取局部特征的能力,减少了特征信息的丢失,实现了较高的故障检测率。采用一维稠密卷积神经网络作为CNN的主体,结合LSTM网络对序列信息变化敏感的特点,在构建更深层网络的同时避免模型过拟合。采用最大互信息(Maximum Mutual Information Coefficient,MMIC)数据预处理方法,提高了数据的局部相关性以及从不同初始条件下PLSTM-CNN模型检测故障的效率。以TE(Tennessee Eastman)过程为研究对象,PLSTM-CNN模型在故障平均检测率和漏报率等指标上明显优于传统循环神经网络。

In order to ensure the safe and stable operation of production processes and avoid losses caused by faults,it is quite important to detect abnormal working conditions in time and diagnose them accurately.Aiming at the complexity of chemical processes,this paper proposes a parallel long and short-term memory network and convolutional neural network(PLSTM-CNN)model for fault detection in chemical production process.By combining the LSTM's ability to extract global features from time series data and the CNN model's ability to extract local features,this model can effectively reduce the loss of feature information and achieve a high fault detection rate.Meanwhile,by using one-dimensional dense convolutional neural network as the main body of CNN and combining the LSTM network's sensitivity to sequence information changes,a deeper network can be built while avoiding model over fitting.Besides,the maximum mutual information coefficient(MMIC)data preprocessing method is adopted to improve the local correlation of the data and improve the efficiency of the PLSTM-CNN model in detecting faults under different initial conditions.Finally,it is shown from the experiment results on Tennessee Eastman(TE)process that the PLSTM-CNN model is obviously superior to the traditional recurrent neural network in such indicators as average failure detection rate and false negative rate.