Soft Computing

, Volume 21, Issue 21, pp 6317–6329 | Cite as

Evolved FCM framework for working condition classification in furnace system

Methodologies and Application
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Abstract

In this paper, an evolved FCM-based clustering method combined with entropy theory is proposed to develop a working condition classification model for the furnace system in coal-fired power plants. To overcome the disadvantage in beforehand determination of clustering number in basic FCM method, Silhouette index is selected as a parameter to evaluate clustering number adaptively in the process. Each time the FCM runs, the selected Silhouette index evaluates the clustering results considering both close and separation degree. Six datasets from UCI machine learning repository are used to certify the effectiveness of the evolved FCM method. Furthermore, pressure sequences from a 300-MW boiler are then discussed as the industrial case study. Three kinds of entropy values, featured from pressure sequence in time–frequency domain, are obtained for further clustering analysis. The clustering results show the strong relationship between boiler’s load and pressure sequences in furnace system. This method can be considered a reference method for data mining in other fluctuating and time-varying sequences.

Keywords

Pressure sequence Entropy Silhouette index Evolved FCM 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Key Laboratory of Energy Thermal Conversion and Control of Ministry of EducationSoutheast UniversityNanjingChina

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