A Multi-Criteria Approach to Maintenance Performance Assessment in a Manufacturing System

doi: 10.14456/mijet.2022.27


  • Desmond Eseoghene Ighravwe Bells University of Technology
  • Sunday Ayoola Oke University of Lagos


Taguchi, Grey-relational analysis, maintenance performance, fuzzy logic, artificial neural network, harmony search algorithm


Nowadays, intensive maintenance research has established procedures for evaluating the performance of industries for control and organisational planning purposes (decision making). However, decisions must be carried out in an accurate, fast and cost-effective manner in compliance with the dynamics of today's industrial activities. This brings the need for intelligent systems for control. Maintenance performance has huge metrics, and the question of how to track these measures in the circumstance of interwoven indices is an urgent problem for all maintenance systems. A novel intelligent approach was developed using the Taguchi method and grey relational analysis for the multi-response optimisation problem to cope with the demand. The key performance indicators used to achieve the optimum response characteristics were the grey-relational grade and the Taguchi's orthogonal array. A comprehensive framework that utilises TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), fuzzy logic, and ANN, respectively, in ranking, quantifying uncertainties and predicting performance is proposed. In achieving optimal global results, differential evolution (DE), big-bang big-crunch (BB-BC) algorithm and harmony search algorithm (HAS) are introduced, fused with ANN in all cases, and the comparison of the hybrids is reported. We found that the differential evolution algorithm performed better than BB-BC and HSA. The principal novelty of the paper is the unique introduction of Taguchi's approach and grey-relational analysis in performance analysis. In the current perspective, the applications of TOPSIS, fuzzy logic, and ANN are also novel. A third novel contribution is the introduction of optimisers in the model framework. It is concluded that this intelligent maintenance performance approach is applicable in industrial environments. The conclusion is supported by the results obtained from real-life manufacturing companies operating in Nigeria, utilised to validate the approach.


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Author Biographies

Desmond Eseoghene Ighravwe, Bells University of Technology

Department of Mechanical and Biomedical Engineering, Bells University of Technology, Ota, Nigeria

Sunday Ayoola Oke, University of Lagos

Department of Mechanical Engineering, University of Lagos, Lagos, Nigeria


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