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

Research Group in Biomedical Engineering (GIIB)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

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.

Original language	English
Title of host publication	Pattern Recognition - 14th Mexican Conference, MCPR 2022, Proceedings
Editors	Osslan Osiris Vergara-Villegas, Vianey Guadalupe Cruz-Sánchez, Juan Humberto Sossa-Azuela, Jesús Ariel Carrasco-Ochoa, José Francisco Martínez-Trinidad, José Arturo Olvera-López
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	35-45
Number of pages	11
ISBN (Print)	9783031077494
DOIs	https://doi.org/10.1007/978-3-031-07750-0_4
State	Published - 2022
Event	14th Mexican Conference on Pattern Recognition, MCPR 2022 - Ciudad Juárez, Mexico Duration: 22 Jun 2022 → 25 Jun 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13264 LNCS

Conference

Conference	14th Mexican Conference on Pattern Recognition, MCPR 2022
Country/Territory	Mexico
City	Ciudad Juárez
Period	22/06/22 → 25/06/22

Bibliographical note

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

Keywords

Computational intelligence
Data imputation
Fault diagnosis
Industrial plants
Missing data
Noise

Access to Document

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

Cite this

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 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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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. in 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, Mexico, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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.

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KW - Missing data

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

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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. In Vergara-Villegas OO, Cruz-Sánchez VG, Sossa-Azuela JH, Carrasco-Ochoa JA, Martínez-Trinidad JF, Olvera-López JA, editors, 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