A multi‐block NMF model for non‐Gaussian process monitoring based on the adaptive partition non‐negative matrix factorization and Bayesian inference. (5th October 2020)

• Record Type: Journal Article
• Authors: Wang, Yan; Li, Shang; Ling, Dan; Yuan, Shi‐meng; Gu, Xiao‐guang
• Is Part Of: Canadian journal of chemical engineering. Volume 99:Number 2(2021)
• Page Start: 543
• Page End: 557
• Publication Date: 2020-10-05
• Subjects: APNMF -- Bayesian inference -- non‐Gaussian process monitoring -- weighted RBC; Chemical engineering -- Periodicals; Technology -- Periodicals; 660.05
• DOI: 10.1002/cjce.23858
• Languages: English
• ISSNs: 0008-4034

Abstract

Abstract: Non‐negative matrix factorization (NMF) is a novel technique for dimension‐reduction, which can be used to process data of non‐Gaussian and Gaussian efficiently. A global NMF model is inappropriate for the whole process, since it neglects the local information and monitoring results are often hard to be interpreted. On the basis of adaptive partition non‐negative matrix factorization (APNMF) and Bayesian inference, a multi‐block NMF model for non‐Gaussian process monitoring is put forward to detect and isolate the faults effectively. Using APNMF method, the original variables in different fault states can be adaptively divided into multiple sub‐blocks, and on this basis, the NMF monitoring model of each sub‐block is formed. Then, two new statistics are constructed by Bayesian inference to supply an intuitive display. Finally, a weighted reconstruction‐based contribution (RBC) plot method is presented to reduce the smearing effect and find out the main causes of these faults. This method makes full use of the local and global information of process data and improves the effectiveness of process monitoring. The validity and feasibility of the proposed method will be proved by an example of a numerical process, a Tennessee Eastman (TE) benchmark process and a continuous stirred‐tank reactor (CSTR) process.