Abstract:

Aiming at the non-linear and dynamic characteristics of intermittent process data, a process fault monitoring method based on recurrent autoencoder (RAE) is proposed. To establish monitoring model, an autoencoder is constructed by using long short-term memory (LSTM) recurrent neural network. Compared with the traditional autoencoder, the proposed method can effectively extract the dynamic correlation information between time series samples. Firstly, a three-step expansion method combining batch expansion and variable expansion are used to expand the batch process data into two dimensions, and input sequences for modeling is obtained by sliding window sampling. Then, LSTM is used to reconstruct the input sequences to train an autoencoder model. Moreover, the Squared prediction error (SPE) statistics are constructed based on reconstruction error to achieve on-line monitoring. Finally, the proposed method is applied to penicillin fermentation process for simulation experiment and recombinant Escherichia coli fermentation process monitoring. The results show that the method can detect faults in time and has better monitoring performance.

Key words: algorithm, dynamic modeling, neural networks, LSTM, process monitoring, recurrent autoencoder

摘要：

针对间歇过程数据非线性、动态性特征，提出一种基于循环自动编码器(recurrent autoencoder，RAE)的过程故障监测方法。采用长短时记忆(long short-term memory，LSTM)循环神经网络构建自动编码器建立监控模型，相比传统自动编码器，其能有效挖掘时序样本间的动态关联信息。该方法首先利用批次展开与变量展开相结合的三步展开方法将间歇过程数据展开成二维，并通过滑动窗采样得到模型输入序列；然后使用LSTM构建自动编码器，重构输入序列。进一步，利用重构误差构造平方预测误差(squared prediction error, SPE)统计量实现在线监测。最后将所提方法应用于青霉素发酵仿真和重组大肠杆菌发酵过程监测，结果表明，该方法能及时监测到故障，具有较好的监测性能。

关键词: 算法, 动态建模, 神经网络, LSTM, 过程监测, 循环自动编码器