基于一维卷积自编码器—高斯混合模型的间歇过程故障检测

Fault Detection Method for a Batch Process Based on a One-dimensional Convolution Autoencoder and Gaussian Mixture Model

传统基于数据驱动的间歇过程故障检测方法往往需要对数据的分布进行假设,其模型多阶段划分不精确,导致故障检测率受到影响.对此提出一种基于一维卷积自编码器—高斯混合模型(One dimensional convolution-auto encoder-Gaussian mixture model,1DC-AE-GMM)的检测新方法.该方法不需要对原始数据进行假设,首先对原始数据进行等长和缩放处理,并以最小重构误差的原则在具有卷积和多个中间层的深层神经网络上进行训练,以非线性的方式自动、精确地进行阶段划分和特征提取;然后在网络的编码层上建立高斯混合模型并进行聚类,在提取特征的同时大大减少了建立模型的计算量;最后结合马氏距离提出全局概率检测指标,实现故障检测.通过在一类半导体蚀刻过程的仿真实验,结果表明该方法可以有效地提高故障检测率.

Traditional methods of fault detection in batch processes that are data-driven often need to make assumptions about the distribution of the process data. The models in those methods are not accurate in multi-stage partitioning, which have an impact on failure detection rate. A novel one-dimensional convolution autoencoder-Gaussian mixture model (1DC-AE-GMM) method is proposed in this paper to deal with this problem. This method does not require assumptions about the raw data. First, the original data are processed with equal length and scaling and the minimum reconfiguration error is used to train the deep neural network with convolution and multiple middle layers, which carries out phase division and feature extraction automatically and accurately in a non-linear way. Then, the Gaussian mixture model and clustering are set up on the coding layer of the network and the computational quantity of the model is reduced greatly while the feature is extracted. Finally, the global probability detection index is presented with a Markov distance and fault detection is realized. The results show that this method can effectively improve the fault detection rate by simulating experiments on a semiconductor etching process.