Abstract
The operation and maintenance of industrial plants constitute a major concern among managers, scientists, and engineers. To allow the prioritization of maintenance actions for improved continuous equipment operation, this paper presents a novel method focused on identifying the most critical components of a plant’s systems. In a complex engineering approach, this method uses tools and techniques borrowed from several fields of knowledge, including an innovative technique devised by the authors, as well as a multi-criteria decision method in the criticality determination process. To illustrate the method, a case study is presented considering a 150 MW Kaplan hydro generator from a hydroelectric power plant located in the north of Brazil. An uncertainty analysis is presented, considering variations in the scores of each criterion, to verify the robustness of the proposed method. The results show that the method responds in a solid and structured way, allowing decision making from qualitative and quantitative data and eliciting the opinion of experts on the factors that can affect the degradation of the performance of a system. In addition, the method’s robustness is confirmed since the uncertainty propagation related to the criteria scores is small for the items classified as most significant from its application.
Highlights
Maintenance is undoubtedly an important sector for the achievement of companies’ organizational objectives
It is observed that in most of the works found in the literature regarding this subject, the criteria used to Maintenance Significant Items (MSI) identification are generally based on concepts that consider the occurrence of the failure, and not the possibility of preventing or anticipating it, seeking to use monitoring data according to Industry 4.0 concepts [22], in the face of a higher demand for productivity and maintenance efficiency
This paper presented a new method for defining the MSI of a system
Summary
Adherbal Caminada Netto 1 , Arthur Henrique de Andrade Melani 1 , Carlos Alberto Murad 1 , Miguel Angelo de Carvalho Michalski 1 , Gilberto Francisco Martha de Souza 1, * and Silvio Ikuyo Nabeta 2.
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