Fault detection of chemical process based on parallel connection PLSTM-CNN
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摘要: 为了保证生产过程的安全稳定运行,避免因故障导致的损失,及时地检测出异常工况并准确地对异常工况进行诊断具有十分重要的研究意义。针对化工过程的复杂性,本文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模型擅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。采用了最大互信息(Maximum mutual information coefficient,MMIC)数据预处理方法,提高了数据的局部相关性,提高了PLSTM-CNN模型从不同初始条件下检测故障的效率。以田纳西州伊斯曼(Eastman Process of Tennessee,TE)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明显优于传统循环神经网络。
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关键词:
- 故障检测 /
- 一维稠密卷积神经网络 /
- 长短时记忆网络 /
- 互信息 /
- TE过程
Abstract: In order to ensure the safe and stable operation of the production process and avoid losses due to failures, timely detection of abnormal conditions and accurate diagnosis of abnormal conditions are of very important research significance. Aiming at the complexity of the chemical process, this paper proposes a parallel long and short-term memory network and convolutional neural network (PLSTM-CNN) model for fault detection in the chemical production process. This model effectively combines the LSTM's ability to extract global features from time series data and the CNN model's ability to extract local features, reducing the loss of feature information and achieving a higher fault detection rate. The one-dimensional dense convolutional neural network is used as the main body of CNN, combined with the LSTM network's sensitivity to sequence information changes, to avoid model overfitting while building a deeper network. 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. Taking the Eastman Process of Tennessee (TE) process in Tennessee as the research object, the PLSTM-CNN model is significantly better than the traditional recurrent neural network in indicators such as the average failure detection rate and the false negative rate. -
表 1 故障检测结果比较
Table 1. The comparison of fault detection results
故障类型 FDR FPR 2D-CNN LSTM PLSTM-CNN 2D-CNN LSTM PLSTM-CNN Normal 0.91 0.96 0.96 0.09 0.04 0 Fault1 1.0 1.0 1.0 0 0 0 Fault2 1.0 1.0 1.0 0 0 0 Fault3 0.48 0.75 0.92 0.44 0.15 0.08 Fault4 1.0 1.0 1.0 0 0 0 Fault5 1.0 1.0 1.0 0 0 0 Fault6 1.0 1.0 1.0 0 0 0 Fault7 1.0 1.0 1.0 0 0 0 Fault8 0.80 1.0 1.0 0.2 0 0 Fault9 0.34 0.36 0.85 0.58 0.23 0.18 Fault10 0.93 0.94 0.92 0.06 0.02 0.01 Fault11 0.96 0.93 0.97 0.04 0.03 0.02 Fault12 0.95 0.98 0.98 0.05 0.02 0.01 Fault13 0.82 0.95 0.96 0.18 0.04 0.04 Fault14 0.84 0.92 0.96 0.16 0.05 0.04 Fault15 0.04 0.12 0.37 0.8 0.64 0.51 Fault16 0.09 0.18 0.33 0.75 0.62 0.48 Fault17 0.96 0.97 1.0 0.04 0.02 0.03 Fault18 1.0 1.0 0.98 0 0 0 Fault19 1.0 1.0 1.0 0 0 0 Fault20 1.0 1.0 1.0 0 0 0 Average 0.83 0.86 0.914 0.16 0.088 0.066 表 2 训练、推理时间比较
Table 2. Comparison of training and inference time
Model Training time for
one epochs(s)Reasoning time for
one epochs(ms)1D-CNN 2.54 10 2D-CNN 65 200 LSTM 3 12 串联
LSTM-CNN3.8 20 并联
LSTM-CNN4.2 25 表 3 小样本平均故障检测率
Table 3. Average failure detection rate of small samples
Model FDR FPR 1D-CNN 83.4% 0.09 2D-CNN 78.4% 0.07 LSTM 84.1% 0.085 串行LSTM-CNN 84.9% 0.12 并行LSTM-CNN 90.2% 0.054 -
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