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

Francisco Javier Ortiz Ortiz; Adrián Rodríguez-Ramos; Orestes Llanes-Santiago

doi:10.1007/978-3-031-07750-0_4

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

Francisco Javier Ortiz Ortiz, Adrián Rodríguez-Ramos, Orestes Llanes-Santiago

Grupo de Investigacion en Ingeniería Biomédica (GIIB)

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

3 Citas (Scopus)

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	Pattern Recognition - 14th Mexican Conference, MCPR 2022, Proceedings
Editores	Osslan Osiris Vergara-Villegas, Vianey Guadalupe Cruz-Sánchez, Juan Humberto Sossa-Azuela, Jesús Ariel Carrasco-Ochoa, José Francisco Martínez-Trinidad, José Arturo Olvera-López
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	35-45
Número de páginas	11
ISBN (versión impresa)	9783031077494
DOI	https://doi.org/10.1007/978-3-031-07750-0_4
Estado	Publicada - 2022
Evento	14th Mexican Conference on Pattern Recognition, MCPR 2022 - Ciudad Juárez, México Duración: 22 jun. 2022 → 25 jun. 2022

Serie de la publicación

Nombre	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen	13264 LNCS
ISSN (versión impresa)	0302-9743
ISSN (versión digital)	1611-3349

Conferencia

Conferencia	14th Mexican Conference on Pattern Recognition, MCPR 2022
País/Territorio	México
Ciudad	Ciudad Juárez
Período	22/06/22 → 25/06/22

Nota bibliográfica

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Acceder al documento

10.1007/978-3-031-07750-0_4

Otros archivos y enlaces

Enlace a la publicación en Scopus

Citar esto

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. En O. O. Vergara-Villegas, V. G. Cruz-Sánchez, J. H. Sossa-Azuela, J. A. Carrasco-Ochoa, J. F. Martínez-Trinidad, & J. A. Olvera-López (Eds.), Pattern Recognition - 14th Mexican Conference, MCPR 2022, Proceedings (pp. 35-45). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13264 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-07750-0_4

Ortiz Ortiz, Francisco Javier ; Rodríguez-Ramos, Adrián ; Llanes-Santiago, Orestes. / A Robust Fault Diagnosis Method in Presence of Noise and Missing Information for Industrial Plants. Pattern Recognition - 14th Mexican Conference, MCPR 2022, Proceedings. editor / Osslan Osiris Vergara-Villegas ; Vianey Guadalupe Cruz-Sánchez ; Juan Humberto Sossa-Azuela ; Jesús Ariel Carrasco-Ochoa ; José Francisco Martínez-Trinidad ; José Arturo Olvera-López. Springer Science and Business Media Deutschland GmbH, 2022. pp. 35-45 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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 = "{Ortiz Ortiz}, {Francisco Javier} and Adri{\'a}n Rodr{\'i}guez-Ramos and Orestes Llanes-Santiago",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 14th Mexican Conference on Pattern Recognition, MCPR 2022 ; Conference date: 22-06-2022 Through 25-06-2022",

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Ortiz Ortiz, FJ, Rodríguez-Ramos, A & Llanes-Santiago, O 2022, A Robust Fault Diagnosis Method in Presence of Noise and Missing Information for Industrial Plants. En OO Vergara-Villegas, VG Cruz-Sánchez, JH Sossa-Azuela, JA Carrasco-Ochoa, JF Martínez-Trinidad & JA Olvera-López (eds.), Pattern Recognition - 14th Mexican Conference, MCPR 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13264 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 35-45, 14th Mexican Conference on Pattern Recognition, MCPR 2022, Ciudad Juárez, México, 22/06/22. https://doi.org/10.1007/978-3-031-07750-0_4

A Robust Fault Diagnosis Method in Presence of Noise and Missing Information for Industrial Plants. / Ortiz Ortiz, Francisco Javier; Rodríguez-Ramos, Adrián; Llanes-Santiago, Orestes.
Pattern Recognition - 14th Mexican Conference, MCPR 2022, Proceedings. ed. / Osslan Osiris Vergara-Villegas; Vianey Guadalupe Cruz-Sánchez; Juan Humberto Sossa-Azuela; Jesús Ariel Carrasco-Ochoa; José Francisco Martínez-Trinidad; José Arturo Olvera-López. Springer Science and Business Media Deutschland GmbH, 2022. p. 35-45 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13264 LNCS).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

TY - GEN

T1 - A Robust Fault Diagnosis Method in Presence of Noise and Missing Information for Industrial Plants

AU - Ortiz Ortiz, Francisco Javier

AU - Rodríguez-Ramos, Adrián

AU - Llanes-Santiago, Orestes

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Computational intelligence

KW - Data imputation

KW - Fault diagnosis

KW - Industrial plants

KW - Missing data

KW - Noise

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U2 - 10.1007/978-3-031-07750-0_4

DO - 10.1007/978-3-031-07750-0_4

M3 - Conference contribution

AN - SCOPUS:85133014177

SN - 9783031077494

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 35

EP - 45

BT - Pattern Recognition - 14th Mexican Conference, MCPR 2022, Proceedings

A2 - Vergara-Villegas, Osslan Osiris

A2 - Cruz-Sánchez, Vianey Guadalupe

A2 - Sossa-Azuela, Juan Humberto

A2 - Carrasco-Ochoa, Jesús Ariel

A2 - Martínez-Trinidad, José Francisco

A2 - Olvera-López, José Arturo

PB - Springer Science and Business Media Deutschland GmbH

T2 - 14th Mexican Conference on Pattern Recognition, MCPR 2022

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Ortiz Ortiz FJ, Rodríguez-Ramos A, Llanes-Santiago O. A Robust Fault Diagnosis Method in Presence of Noise and Missing Information for Industrial Plants. En Vergara-Villegas OO, Cruz-Sánchez VG, Sossa-Azuela JH, Carrasco-Ochoa JA, Martínez-Trinidad JF, Olvera-López JA, editores, Pattern Recognition - 14th Mexican Conference, MCPR 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 35-45. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-07750-0_4