The Batch Process Fault Monitoring Using Adversarial Auto-encoder and K-Nearest Neighbor Rule

In the industrial batch process monitoring domain, the conventional multivariate monitoring methods may not always function well in monitoring faults that have both Non-Linear and Non-Gaussian properties. To enhance the monitoring capability, the adversarial auto-encoder (AAE) was introduced to increase the sensitivity to Non-Gaussian anomalies by projecting non-Gaussian information into a given Gaussian distribution feature space. At the same time, low-dimensional feature space can avoid the problem of “Concentration of measure” and improve the ability to distinguish minor small abnormalities. Therefore, A novel statistic index was constructed in the feature space based on the k-nearest neighbor rule (KNN) to improve the ability of minor fault monitoring. The proposed model is compared with the traditional multivariate statistical process monitoring methods in numerical examples and penicillin fermentation platform, which proves that it has better monitoring ability for minor magnitude and non-Gaussian faults.