Adaptive Fuzzy C-Means clustering in process monitoring

Quite often, quality control models fail because, e.g., the mean values are changing continuously. These kinds of changes, e.g., process drifts due to seasonal fluctuations, are common in an activated sludge waste-water treatment plant in Finland. Different Fuzzy C-Means (FCM) clustering algorithms...

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Bibliographic Details
Published in:Chemometrics and intelligent laboratory systems 1999-01, Vol.45 (1), p.23-38
Main Authors: Teppola, Pekka, Mujunen, Satu-Pia, Minkkinen, Pentti
Format: Article
Language:English
Subjects:
Activated sludge waste-water treatment plant
Adaptive
Chemometrics
FCM
Flexible
PCA
Process monitoring
Pulp and paper mills
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Summary:Quite often, quality control models fail because, e.g., the mean values are changing continuously. These kinds of changes, e.g., process drifts due to seasonal fluctuations, are common in an activated sludge waste-water treatment plant in Finland. Different Fuzzy C-Means (FCM) clustering algorithms were tested in order to cope with these kinds of seasonal effects. Firstly, a Principal Component Analysis (PCA) model was constructed in order to visualize the data set and reduce the dimensionality of the problem. Then, score values of the PCA were used in the FCM. The cluster centers represented the different process conditions (winter and summer seasons). Different algorithms were used to update the cluster centers or to give them some flexibility. The testing of different FCM algorithms was carried out by using a separate test set. The adaptive and the flexible FCM algorithms were compared to the basic non-adaptive FCM. For both cases, modifications are proposed and a simple strategy for updating the cluster centers is given.
ISSN:0169-7439
1873-3239
DOI:10.1016/S0169-7439(98)00087-2