Adaptive multiscale principal components analysis for online monitoring of wastewater treatment
(2002) In Water Science and Technology 45(4-5). p.227-235- Abstract
- Fault detection and isolation (FDI) are important steps in the monitoring and supervision of industrial processes. Biological wastewater treatment (WWT) plants are difficult to model, and hence to monitor, because of the complexity of the biological reactions and because plant influent and disturbances are highly variable and/or unmeasured. Multivariate statistical models have been developed for a wide variety of situations over the past few decades, proving successful in many applications. In this paper we develop a new monitoring algorithm based on Principal Components Analysis (PCA). It can be seen equivalently as making Multiscale PCA (MSPCA) adaptive, or as a multiscale decomposition of adaptive PCA. Adaptive Multiscale PCA (AdMSPCA)... (More)
- Fault detection and isolation (FDI) are important steps in the monitoring and supervision of industrial processes. Biological wastewater treatment (WWT) plants are difficult to model, and hence to monitor, because of the complexity of the biological reactions and because plant influent and disturbances are highly variable and/or unmeasured. Multivariate statistical models have been developed for a wide variety of situations over the past few decades, proving successful in many applications. In this paper we develop a new monitoring algorithm based on Principal Components Analysis (PCA). It can be seen equivalently as making Multiscale PCA (MSPCA) adaptive, or as a multiscale decomposition of adaptive PCA. Adaptive Multiscale PCA (AdMSPCA) exploits the changing multivariate relationships between variables at different time-scales. Adaptation of scale PCA models over time permits them to follow the evolution of the process, inputs or disturbances. Performance of AdMSPCA and adaptive PCA on a real WWT data set is compared and contrasted. The most significant difference observed was the ability of AdMSPCA to adapt to a much wider range of changes. This was mainly due to the flexibility afforded by allowing each scale model to adapt whenever it did not signal an abnormal event at that scale. Relative detection speeds were examined only summarily, but seemed to depend on the characteristics of the faults/disturbances. The results of the algorithms were similar for sudden changes, but AdMSPCA appeared more sensitive to slower changes. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/340478
- author
- Lennox, J and Rosén, Christian LU
- organization
- publishing date
- 2002
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- multivariate statistical process monitoring, multiscale PCA, fault detection and isolation, adaptive PCA, confidence limits
- in
- Water Science and Technology
- volume
- 45
- issue
- 4-5
- pages
- 227 - 235
- publisher
- IWA Publishing
- external identifiers
-
- wos:000174872000029
- pmid:11936638
- scopus:0036209691
- ISSN
- 0273-1223
- language
- English
- LU publication?
- yes
- id
- 6da79c80-3792-4d46-88e2-b8feb16cb55e (old id 340478)
- date added to LUP
- 2016-04-01 17:04:24
- date last changed
- 2022-01-29 00:06:38
@article{6da79c80-3792-4d46-88e2-b8feb16cb55e,
abstract = {{Fault detection and isolation (FDI) are important steps in the monitoring and supervision of industrial processes. Biological wastewater treatment (WWT) plants are difficult to model, and hence to monitor, because of the complexity of the biological reactions and because plant influent and disturbances are highly variable and/or unmeasured. Multivariate statistical models have been developed for a wide variety of situations over the past few decades, proving successful in many applications. In this paper we develop a new monitoring algorithm based on Principal Components Analysis (PCA). It can be seen equivalently as making Multiscale PCA (MSPCA) adaptive, or as a multiscale decomposition of adaptive PCA. Adaptive Multiscale PCA (AdMSPCA) exploits the changing multivariate relationships between variables at different time-scales. Adaptation of scale PCA models over time permits them to follow the evolution of the process, inputs or disturbances. Performance of AdMSPCA and adaptive PCA on a real WWT data set is compared and contrasted. The most significant difference observed was the ability of AdMSPCA to adapt to a much wider range of changes. This was mainly due to the flexibility afforded by allowing each scale model to adapt whenever it did not signal an abnormal event at that scale. Relative detection speeds were examined only summarily, but seemed to depend on the characteristics of the faults/disturbances. The results of the algorithms were similar for sudden changes, but AdMSPCA appeared more sensitive to slower changes.}},
author = {{Lennox, J and Rosén, Christian}},
issn = {{0273-1223}},
keywords = {{multivariate statistical process monitoring; multiscale PCA; fault detection and isolation; adaptive PCA; confidence limits}},
language = {{eng}},
number = {{4-5}},
pages = {{227--235}},
publisher = {{IWA Publishing}},
series = {{Water Science and Technology}},
title = {{Adaptive multiscale principal components analysis for online monitoring of wastewater treatment}},
volume = {{45}},
year = {{2002}},
}