Supervised and semi-supervised probabilistic learning with deep neural networks for concurrent process-quality monitoring
Supervised and semi-supervised probabilistic learning with deep neural networks for concurrent process-quality monitoring
复制标题
DOI：
10.1016/j.neunet.2020.11.006
复制
发表时间：
2020-12
期刊：
影响因子：
--
通讯作者：
Kai Wang;Xiaofeng Yuan;Junghui Chen;Yalin Wang
中科院分区：
文献类型：
--
作者：
Kai Wang;Xiaofeng Yuan;Junghui Chen;Yalin Wang
并发过程质量监控有助于发现与质量相关的过程异常和与质量无关的过程异常。它特别适用于有缺陷导致质量问题的化工厂。由于培训数据中的质量指标不足，传统的监测策略在化工企业中的应用受到限制。不灵活的模型很难完全捕获强非线性过程质量相关性。此外，确定性模型从过程变量映射到质量，而不考虑任何不确定性。同时，由于产品质量测试通常既耗时又昂贵，因此质量变量的采样率缓慢在化工厂是普遍存在的。针对这些局限性，本文提出了一种基于变分自编码器的概率生成深度学习模型的并行过程质量监测方案。首先建立了监督模型，然后对半监督模型进行了扩展，解决了目标缺失问题。其中，半监督学习算法采用最大似然原理进行最优参数估计，不引入任何超参数。两个实例验证了该方法在并行过程质量监控方面的有效性。
Concurrent process-quality monitoring helps discover quality-relevant process anomalies and quality-irrelevant process anomalies. It especially works well in chemical plants with faults that cause quality problems. Traditional monitoring strategies are limitedly applied in chemical plants because quality targets in training data are insufficient. It is hard for inflexible models to fully capture the strongly nonlinear process-quality correlations. Also, deterministic models are mapped from process variables to qualities without any consideration of uncertainties. Simultaneously, a slow sampling rate for quality variables is ubiquitous in chemical plants since a product quality test is often time-consuming and expensive. Motivated by these limitations, this paper proposes a new concurrent process-quality monitoring scheme based on a probabilistic generative deep learning model developed from variational autoencoder. The supervised model is firstly developed and then the semi-supervised version is extended to solve the issue of missing targets. Especially, the semi-supervised learning algorithm is accomplished with an optimal parameter estimation in the light of maximum likelihood principle and no any hyperparameters are introduced. Two case studies validate that the proposed method effectively outperforms the other comparative methods in concurrent process-quality monitoring.