Fuzzy overall equipment effectiveness and line performance measurement using artificial neural network

Mahsa Fekri Sari, Soroush Avakh Darestani

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Purpose – The overall equipment effectiveness (OEE) is a powerful metric in production as well as one of the methods in evaluating function for measuring productivity in the production process. In the existing method, measuring OEE is based on three main elements consisting availability, performance and quality. The purpose of this paper is to evaluate the recognized metrics of production: OEE and overall line effectiveness (OLE) by using smart systems techniques.
Design/methodology/approach – In this paper, to improve the calculative methods and productivity with three methods: measuring OEE using Mamdani fuzzy inference systems (FIS), measuring OEE using Sugeno FIS, and measuring OLE using FIS and artificial neural networks (ANNs) are proposed.
Findings – The proposed methodologies aim to decrease some weaknesses of OEE and OLE methods by exploiting intelligent system techniques, such as FIS and ANNs. In particular, this research will solve the following issues that occur in manual and automatic data gathering. This technique is an effective way of
measuring OEE and OLE with regard to different weights of losses as well as difference in the weight of the machines. In addition, it allows the operator’s knowledge to take a part in the measurement using uncertain input and output with implementation of linguistic terms. The presented method is the details and
capabilities of those methods in various tested scenarios, and the results have been fully analysed.
Originality/value – In relation to other methodologies, it allows the operator’s knowledge to take part in the measurement using uncertain input and output with implementation of linguistic terms. The presented method is the details and capabilities of those methods in various tested scenarios, and the results have been fully analysed.
Original languageEnglish
Pages (from-to)340-354
Number of pages15
JournalJournal of Quality in Maintenance Engineering
Volume25
Issue number2
DOIs
Publication statusPublished - 7 May 2019
Externally publishedYes

Keywords

  • Fuzzy inference system
  • Total productive maintenance (TPM)
  • Artificial neural networks
  • Overall effectiveness equipment
  • Overall efficiency
  • Fuzzy inference

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