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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bafroui HH, Ohadi A (2014) Application of wavelet energy and Shannon entropy for feature extraction in gearbox fault detection under varying speed conditions. Neurocomputing 133:437–445
Billard L (2008) Some analyses of interval data. J Comput Inf Technol 16:225–233
Calinski T (1968) A dendrite method for cluster analysis. Biometrics 24:207
Caniego FJ, Martin MA, San Jose F (2001) Singularity features of pore-size soil distribution: singularity strength analysis and entropy spectrum. Fract Complex Geom Patterns Scaling Nat Soc 9:305–316
Davies DL, Bouldin DW (1979) Cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 1:224–227
Diez LI, Cortes C, Arauzo I, Valero A (2001) Combustion and heat transfer monitoring in large utility boilers. Int J Therm Sci 40:489–496
Guevel TL, Thomas P (2003) Fuel flexibility and petroleum coke combustion at provence 250 MW CFB. In: Proceedings of 17th international conference on fluidized bed combustion, Florida, USA, pp 643–649
Huang B, Luo Z, Zhou H (2010) Optimization of combustion based on introducing radiant energy signal in pulverized coal-fired boiler. Fuel Process Technol 91:660–668
Jia X, Lu Y (1996) Fuzzy information processing. National University of Defence Technology Press, Changsha
Huang LK, Li ZQ, Sun R, Zhou J (2006) Numerical study on the effect of the Over-Fire-Air to the air flow and coal combustion in a 670 t/h wall-fired boiler. Fuel Process Technol 87:363–371
Kim DW, Lee KH, Lee DH (2004a) On cluster validity index for estimation of the optimal number of fuzzy clusters. Pattern Recognit 37:2009–2025
Kim YI, Kim DW, Lee D, Lee KH (2004b) A cluster validation index for GK cluster analysis based on relative degree of sharing. Inf Sci 168:225–242
Krzanowski WJ, Lai YT (1988) A criterion for determining the number of groups in a data set using sum-of-squares clustering. Biometrics 44:23–34
Kurose R, Makino H, Suzuki A (2004) Numerical analysis of pulverized coal combustion characteristics using advanced low-NOx burner. Fuel 83:693–703
Lee C-L, Jou C-JG (2013) Improving furnace energy efficiency through adjustment of damper angle. Int J Hydrogen Energy 38:2504–2509
Liao TW (2005) Clustering of time series data: a survey. Pattern Recognit 38:1857–1874
Pimentel BA, de Souza RMCR, IEEE (2012) Possibilistic approach to clustering of interval data. In: Proceedings 2012 IEEE international conference on systems, man, and cybernetics, pp 190–195
Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster-analysis. J Comput Appl Math 20:53–65
Shaohua MA, Ying HUA, Xiaobai LI (2007) An analysis of flame signals in a boiler furnace based on a phase space reconstruction. J Eng Therm Energy Power 22(440–442):456
Vikhansky A, Bar-Ziv E, Chudnovsky B, Talanker A, Eddings E, Sarofim A (2004) Measurements and numerical simulations for optimization of the combustion process in a utility boiler. Int J Energy Res 28:391–401
Wang K-M, Zhong N, Zhou H-Y (2014) Activity analysis of depression electroencephalogram based on modified power spectral entropy. Acta Phys Sin 63:533–538
Weira N (2009) Large scale pulverized coal boiler furnace pressure nonlinear characteristics research based on Fractal and chaos theory. Shandong University, Shandong
Zadeh LA (1965) Fuzzy sets. Inf Control 8:338
Zadeh LA (2005) Toward a generalized theory of uncertainty (GTU): an outline. Inf Sci 172:1–40
Zhou Y, Xu T, Hui S, Zhang M (2009) Experimental and numerical study on the flow fields in upper furnace for large scale tangentially fired boilers. Appl Therm Eng 29:732–739
Acknowledgments
This work is granted by the National Natural Science Foundation of China (51176030) and Jiangsu Science and Technology Department (BY2015070-17).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Communicated by V. Loia.
Rights and permissions
About this article
Cite this article
Gu, H., Ren, S., Si, F. et al. Evolved FCM framework for working condition classification in furnace system. Soft Comput 21, 6317–6329 (2017). https://doi.org/10.1007/s00500-016-2184-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-016-2184-0