Title: Application of neurofuzzy framework to maintenance scheduling activity monitoring

Authors: S.A. Oke, S.A. Adeoye, Adeyinka Oluwo, M.O. Oyekeye, S.I. Alozie, A.O. Johnson

Addresses: Department of Mechanical Engineering, Room 10, Mezzanine Complex, Faculty of Engineering, University of Lagos, Akoka-Yaba, Lagos, Nigeria. ' Department of Mechanical Engineering, Former Counselling Building, Faculty of Engineering, University of Lagos, Akoka-Yaba, Lagos, Nigeria. ' Department of Mechanical Engineering, Former Counselling Building, Faculty of Engineering, University of Lagos, Akoka-Yaba, Lagos, Nigeria. ' Department of Mechanical Engineering, Former Counselling Building, Faculty of Engineering, University of Lagos, Akoka-Yaba, Lagos, Nigeria. ' Department of Mechanical Engineering, Former Counselling Building, Faculty of Engineering, University of Lagos, Akoka-Yaba, Lagos, Nigeria. ' C/o Department of Mechanical Engineering, Room 10, Mezzanine Complex, Faculty of Engineering, University of Lagos, Akoka-Yaba, Lagos, Nigeria

Abstract: This paper applies neurofuzzy principles to a maintenance scheduling framework that involves selection of alternate preventive maintenance and operations such that the total preventive maintenance cost is minimised. Fuzzy logic, which incorporates an alternative way of thinking that allows modelling complex systems using a higher level of abstraction originating from our knowledge and experience, is incorporated into the overall framework presented. This is achieved through the fusion of neural networks and fuzzy logic in neurofuzzy models as applied here to allow uncertainty reasoning with linguistic inputs and interpretation of results in terms of natural language. This paper shows why neurofuzzy models should be applied on the problem described, and how the neurofuzzy principles are applied in a shipping organisation for preventive maintenance scheduling of a fleet of ships. The work demonstrated how uncertainty representation and fuzzy inferences in relation to ship maintenance scheduling could be established.

Keywords: neurofuzzy modelling; maintenance scheduling; optimal schedule; cost minimisation; fuzzy logic; artificial neural networks; ANNs; shipping; maintenance monitoring; total preventive maintenance; TPM; ship maintenance.

DOI: 10.1504/IJADS.2009.027933

International Journal of Applied Decision Sciences, 2009 Vol.2 No.3, pp.299 - 326

Available online: 20 Aug 2009 *

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