An efficient quality-related fault diagnosis method for real-time multimode industrial process
type: Item
instance of: scholarly article
title: An efficient quality-related fault diagnosis method for real-time multimode industrial process
zbMATH Open document ID: 1400.93286
DOI: 10.1155/2017/9560206
author: Kaixiang Peng
author: Bingzheng Wang
author: Jie Dong
published in: Journal of Control Science and Engineering
publication date: 2018-10-15T00:00:00Z
review text: Summary: Focusing on quality-related complex industrial process performance monitoring, a novel multimode process monitoring method is proposed in this paper. Firstly, principal component space clustering is implemented under the guidance of quality variables. Through extraction of model tags, clustering information of original training data can be acquired. Secondly, according to multimode characteristics of process data, the monitoring model integrated Gaussian mixture model with total projection to latent structures is effective after building the covariance description form. The Multimode Total Projection to Latent Structures (MTPLS) model is the foundation of problem solving about quality-related monitoring for multimode processes. Then, a comprehensive statistics index is defined which is based on the posterior probability of the monitored samples belonging to each Gaussian component in the Bayesian theory. After that, a combined index is constructed for process monitoring. Finally, motivated by the application of traditional contribution plot in fault diagnosis, a gradient contribution rate is applied for analyzing the variation of variable contribution rate along samples. Our method can ensure the implementation of online fault monitoring and diagnosis for multimode processes. Performances of the whole proposed scheme are verified in a real industrial, Hot Strip Mill Process (HSMP) compared with some existing methods.
Mathematics Subject Classification ID: 93E03
Mathematics Subject Classification ID: 93C83
Mathematics Subject Classification ID: 90B35
Mathematics Subject Classification ID: 93B35
Mathematics Subject Classification ID: 93B03
zbMATH DE Number: 6954299
zbMATH Keywords: fault diagnosis
zbMATH Keywords: multimode industrial process
zbMATH Keywords: hot strip mill process
Wikidata QID: Q59145657
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/9560206
OpenAlex ID: W2592905300
cites work: Fault detection and diagnosis in industrial systems
cites work: Reconstruction-based contribution for process monitoring
description: scientific article; zbMATH DE number 6954299