Fuzzy rule-based classifier for Fault Prediction in a Thermoelectric Unit

Authors

  • Otacílio José Pereira PhD student in Industrial Engineering at Federal University of Bahia
  • Cristiano Hora de Oliveira Fontes Universidade Federal da Bahia
  • Carlos Arthur M. Teixeira Cavalcante Universidade Federal da Bahia
  • Sérgio Torres Sá Barretto Petróleo Brasileiro S.A. (PETROBRAS)
  • Luciana de Almeida Pacheco Petróleo Brasileiro S.A. (PETROBRAS)

Keywords:

fuzzy inference systems, pattern recognition, multivariate time series, fault prediction.

Abstract

Pattern recognition from data is a potential alternative for the extraction of knowledge about processes and it may be useful for predicting failures, control and support decision making, among others. The knowledge extracted can be used to implement models based on Artificial Intelligence such as Fuzzy Inference Systems (FIS). Tools from Information Technology (IT) and automation techniques can also be used in data-based approaches to enable the storage and handling of large amounts of historical process data. This paper presents the implementation of a fuzzy inference system for fault prediction in a gas turbine of a thermoelectric unit. The first step comprised the pattern recognition through the clustering of multivariate time series obtained from the Plant Information Management System (PIMS). The second step comprised the development of a FIS using a data-based approach to define the membership functions and rules. The results showed the potential of the fuzzy model to predict the probability of failure during the start of the turbine this presenting a feasible alternative to support decision-making at operational level.

Downloads

Download data is not yet available.

Downloads

Published

2014-02-05

How to Cite

Pereira, O. J., Fontes, C. H. de O., Cavalcante, C. A. M. T., Barretto, S. T. S., & Pacheco, L. de A. (2014). Fuzzy rule-based classifier for Fault Prediction in a Thermoelectric Unit. Brazilian Journal of Operations & Production Management, 10(2), 79–90. Retrieved from https://bjopm.org.br/bjopm/article/view/V10N2A8

Issue

Vol. 10 No. 2 (2013): December, 2013

Section

Articles

License

Authors who publish with this journal agree to the following terms:

- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

- Authors must have a written permission from any third-party materials used in the article, such as figures and graphics. The permission must explicitly allow authors to use the materials. The permission should be submitted with the article, as a supplementary file.

- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after BJO&PM publishes it (See The Effect of Open Access).