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Fuzzy Classification of Industrial Data for Supervision of a Dewatering Machine: Implementation Details and Results

  • Carlos M. Sánchez MEmail author
  • Henry O. Sarmiento M
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1096)

Abstract

In this document, real data collected in an industrial process are studied and analyzed, with the intention of improving the process supervision seeking for operational efficiency and saving resources, emphasizing in the information cleaning process using basic statistics and data analysis based on non-supervised clustering algorithms: Lamda, GK means and Fuzzy C-means. A general data cleaning procedure for use in industrial environments is suggested. The procedure proposed is followed in a case for a centrifuge machine for mud treatment, three versions of fuzzy classifiers were tested where fuzzy, c-means was finally selected and a result is obtained that permits detecting an inefficient operating state, in some cases the machine was running at a normal current and spending energy and other resources for a long period and the mud was not treated properly, the exit mud was practically the same as the mud at the entrance. The trained classifier has been implemented directly in the PLC used to control the machine, and the results of online classification have been verified showing that states correspond with the process behavior.

Keywords

Data cleaning Industrial data Fuzzy clustering 

References

  1. 1.
    Wang, Y., Byrd, T.A., Kung, L.: Big data analytics: understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting & Social Change (2016)Google Scholar
  2. 2.
    Dresner Advisory Services 2018 Big Data Analytics Market Study. Big Data Analytics Adoption Soared in the Enterprise in 2018. Forbes (2018)Google Scholar
  3. 3.
    Cohen, M.C.: Big data and service operations. Prod. Oper. Manag. 27(9), 1709–1723 (2018)CrossRefGoogle Scholar
  4. 4.
    Munir, M., Baumbach, S., Gu, Y., Dengel, A., Ahmed, S.: Data analytics: industrial perspective & solutions for streaming data. In: Data Mining in Time Series and Streaming Databases, Kaiserslautern, Germany, World Scientific, pp. 144–168 (2018)Google Scholar
  5. 5.
    Xu, S., Lu, B., Baldea, M., Wojsznis, W.: Data cleaning in the process industries. Rev. Chem. Eng. 31(5), 453–490 (2015)CrossRefGoogle Scholar
  6. 6.
    Torabi, M., Hashemi, S., Saybani, R., Shamshirband, S., Mosavi, A.: A Hybrid Clustering and Classification Technique for Forecasting Short-Term Energy Consumption. Wiley Online Library (2018)Google Scholar
  7. 7.
    Davenport, T.: What to Ask Your “Numbers People”. Harvard Bussines Review, pp. 2–3 (2014)Google Scholar
  8. 8.
    Lückeheide, S., Velásquez, J., Cerda, L.: Segmentación de los contribuyentes que declaran IVA aplicando Herramientas de Clustering. Revista Ingeniería de Sistemas 21, 87–110 (2007)Google Scholar
  9. 9.
    Venkatasubramanian, V., Rengaswamy, R., Kavuri, S.N., Yin, K.: A review of process fault detection and diagnosis: Part III: process history based methods. Comput. Chem. Eng. 27(3), 327–346 (2003)CrossRefGoogle Scholar
  10. 10.
    Sarmiento, H., Isaza, C., Kempowsky-Hamon, T., Le Lann, M.V.: Situation prediction based on fuzzy clustering for industrial complex processes. Inf. Sci. 279, 785–804 (2014)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Heil, J., Haring, V., Marschner, B., Stumpe, B.: Advantages of fuzzy k-means over k-means clustering in the classification of diffuse reflectance soil spectra: a case study with West African soils. Geoderma 337, 11–21 (2018)CrossRefGoogle Scholar
  12. 12.
    Aguilar-Martín, J., Lopez De Mantaras, R.: The process of classification and learning the meaning of linguistic descriptors of concepts. In: Gupta, M.M., Sanchez, E. (eds.) Approximate Reasoning in Decision Analysis, pp. 165–175. North Holland (1982)Google Scholar
  13. 13.
    Aguilar-Martin, J., Aguado, C.: A mixed qualitative-quantitative selflearning classification technique applied to diagnosis. In: QR’99 the Thirteenth International Workshop on Qualitative Reasoning, Chris Price, pp. 124–128 (1999)Google Scholar
  14. 14.
    Zadeh, L.: Fuzzy sets as a basis of theory of possibility. In: Fuzzy Sets and Systems 1, pp. 3–28. North Hollad, Berkeley (1978)Google Scholar
  15. 15.
    Piera, N., Aguilar, J.: Controlling selectivity in non-standard pattern recognition algorithms. IEEE Trans. Syst. Man Cybernetics 21(1), 71–82 (1991)CrossRefGoogle Scholar
  16. 16.
    Rakoto-Ravalontsalama, N., Aguilar-Martin, J.: Automatic clustering for symbolic evaluation for dynamical system supervision. In: 1992 American Control Conference, Chicago, USA (1992)Google Scholar
  17. 17.
    Hedjazi, L., Aguilar-Martin, J.: Similarity-margin based feature selection for symbolic interval data. Pattern Recogn. Lett. 32(4), 578–585 (2010)CrossRefGoogle Scholar
  18. 18.
    Bezdek, J.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Publishing Corporation, New York (1981)CrossRefGoogle Scholar
  19. 19.
    Gustafson, D., Kessell, W.: Fuzzy clustering with a fuzzy covariance matrix. In: IEEE Conference on Decision and Control Including the 17th Symposium on Adaptive Processes, University of California, Berkeley, pp. 761–766 (1978)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Politécnico Colombiano Jaime Isaza CadavidMedellínColombia

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