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A Robust Fault Diagnosis Method in Presence of Noise and Missing Information for Industrial Plants

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Pattern Recognition (MCPR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13264))

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Abstract

Fault diagnosis systems are necessary in industrial plants to reach high economic profits and high levels of industrial safety. For achieving these aims, it is necessary a fast detection and identification of faults that occur in the plants. However, the performance of the fault diagnosis systems, are affected by the presence of noise and missing information on the measured variables from the industrial systems. In this paper, a novel methodology for fault diagnosis in industrial plants is proposed by using computational intelligence tools. The proposal presents a robust behavior in the presence of missing data and noise in the measurements by achieving high levels of performance. The imputation process prior to the diagnosis of failures is carried out online, this being one of the advantages.

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Author information

Authors and Affiliations

  1. Programa de Maestría en Electrónica y Automatización, Universidad Politécnica Salesiana, Cuenca, Ecuador

    Francisco Javier Ortiz Ortiz

  2. Dpto. de Automática y Computación, Universidad Tecnológica de La Habana José Antonio Echeverría, CUJAE, La Habana, Cuba

    Adrián Rodríguez-Ramos & Orestes Llanes-Santiago

Authors
  1. Francisco Javier Ortiz Ortiz
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  2. Adrián Rodríguez-Ramos
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  3. Orestes Llanes-Santiago
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Corresponding author

Correspondence to Francisco Javier Ortiz Ortiz .

Editor information

Editors and Affiliations

  1. Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Mexico

    Osslan Osiris Vergara-Villegas

  2. Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Mexico

    Vianey Guadalupe Cruz-Sánchez

  3. Instituto Politécnico Nacional, Mexico City, Mexico

    Juan Humberto Sossa-Azuela

  4. Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, Mexico

    Jesús Ariel Carrasco-Ochoa

  5. Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, Mexico

    José Francisco Martínez-Trinidad

  6. Autonomous University of Puebla, Puebla, Mexico

    José Arturo Olvera-López

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Ortiz Ortiz, F.J., Rodríguez-Ramos, A., Llanes-Santiago, O. (2022). A Robust Fault Diagnosis Method in Presence of Noise and Missing Information for Industrial Plants. In: Vergara-Villegas, O.O., Cruz-Sánchez, V.G., Sossa-Azuela, J.H., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2022. Lecture Notes in Computer Science, vol 13264. Springer, Cham. https://doi.org/10.1007/978-3-031-07750-0_4

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  • DOI: https://doi.org/10.1007/978-3-031-07750-0_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07749-4

  • Online ISBN: 978-3-031-07750-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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