Batch Process Monitoring Based on Multi-stage Fourth Order Moment Stacked Autoencoder
作者单位：Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System
会议名称：《第32届中国控制与决策会议》
会议日期：2020年
学科分类：08[工学] 0802[工学-机械工程] 080201[工学-机械制造及其自动化]
关 键 词：Batch process Fault monitoring non-Gaussian Multi-stage Stacked Autoencoder
摘 要：Fault monitoring can find out-of-control conditions of equipment operation in a timely manner, which is essential for eliminating faults and for stable operation of industrial systems in batch processes. Many conventional data-driven fault detection methods focus less on the non-Gaussian and Multi-stage characteristics of batch process data, which may result in degradation of monitoring performance. In this paper, a Multi-stage Fourth Order Moment Staked Autoencoder（M-FOM-SAE） is designed to solve the above problems. The proposed method firstly automatically determines the number of clusters and divides the batch process into multiple stages. After that, the FOM-SAE model is established in each sub-stage, which can not only effectively learn the nonlinear features of process data, but also extract the non-Gaussian information. The proposed strategy is applied to real-world industrial processes. Experimental results indicate that it can better capture the non-Gaussian and Multi-stage characteristics of process data, and improve the ability to monitor abnormalities.