An approach to robust fault diagnosis in mechanical systems using computational intelligence

Adrián Rodríguez Ramos (), José M. Bernal de Lázaro (), Alberto Prieto-Moreno (), Antônio José Silva Neto () and Orestes Llanes-Santiago ()
Additional contact information
Adrián Rodríguez Ramos: Universidad Tecnológica de la Habana “José Antonio Echeverría” (CUJAE)
José M. Bernal de Lázaro: Universidad Tecnológica de la Habana “José Antonio Echeverría” (CUJAE)
Alberto Prieto-Moreno: Universidad Tecnológica de la Habana “José Antonio Echeverría” (CUJAE)
Antônio José Silva Neto: Instituto Politécnico da Universidade do Estado do Rio de Janeiro (IPRJ/UERJ)
Orestes Llanes-Santiago: Universidad Tecnológica de la Habana “José Antonio Echeverría” (CUJAE)

Journal of Intelligent Manufacturing, 2019, vol. 30, issue 4, No 6, 1615 pages

Abstract: Abstract In this paper a novel approach to design robust fault diagnosis systems in mechanical systems using historical data and computational intelligence techniques is presented. First, the pre-processing of the data to remove the outliers is performed with the aim of reducing the classification errors. To accomplish this objective, the Density Oriented Fuzzy C-Means (DOFCM) algorithm is used. Later on, the Kernel Fuzzy C-Means (KFCM) algorithm is used to achieve greater separability among the classes, and reducing the classification errors. Finally, an optimization process of the parameters used in the training state by the DOFCM and KFCM for improving the classification results is developed using the bioinspired algorithm Ant Colony Optimization. The proposal was validated using the DAMADICS (Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems) benchmark. The satisfactory results obtained indicate the feasibility of the proposal.

Keywords: Robust fault diagnosis; Mechanical systems; Computational intelligence; Fuzzy clustering techniques; Optimal parameters (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-017-1343-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:30:y:2019:i:4:d:10.1007_s10845-017-1343-1

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-017-1343-1

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().