Optimal Maintenance Thresholds to Perform Preventive Actions by Using Multi-Objective Evolutionary Algorithms

Abstract

Maintenance has always been a key activity in the manufacturing industry because of its economic consequences. Nowadays, its importance is increasing thanks to the "Industry 4.0" or "fourth industrial revolution". There are more and more complex systems to maintain, and maintenance management must gain efficiency and effectiveness in order to keep all these devices in proper conditions. Within maintenance, Condition-Based Maintenance (CBM) programs can provide significant advantages, even though often these programs are complex to manage and understand. For this reason, several research papers propose approaches that are as simple as possible and can be understood by users and modified by experts. In this context, this paper focuses on CBM optimization in an industrial environment, with the objective of determining the optimal values of preventive intervention limits for equipment under corrective and preventive maintenance cost criteria. In this work, a cost-benefit mathematical model is developed. It considers the evolution in quality and production speed, along with condition based, corrective and preventive maintenance. The cost-benefit optimization is performed using a Multi-Objective Evolutionary Algorithm. Both the model and the optimization approach are applied to an industrial case.

Development and Application of a Multi-Objective Tool for Thermal Design of Heat Exchangers Using Neural Networks

“…A multi-objective problem (MOP) can be stated as follows [43,44] minimise/maximise y = f(x) = ( f 1 (x), f 2 (x), . .…”

Multi Equipment Condition Based Maintenance Optimization Using Multi-Objective Evolutionary Algorithms

“…In this work, a new method was developed to optimize CBM activities of a simplified multi-equipment hub cap production system. The work is based on the multi-component but single equipment model presented in [9]; as added value, this research extends it using DES so that in this case it is possible to perform a multi-equipment optimization. In previous studies [10,11], a PM optimization [10] and a CBM optimization based on a different deterioration model [11] were presented.…”

“…Moreover, PAR model assumes that the maintenance reduces the age of the component proportionally by a ε factor, between the moment after executing the previous PM activity (w + m−1 ) and the moment before the last one (w − m ). The assumptions to obtain the expressions for the PAR model are the same as in [12]. The results of these assumptions are stated in Figure 2, where the age of a component w is compared with its chronological time.…”

Oportunidades de aplicações de Business Intelligence no contexto da indústria 4.0: revisão sistemática da literatura 2015-2020

“…Entre as tecnologias destacadas, a análise de dados e inteligência artificial, tanto no gerenciamento de produção como de manutenção com um nível de impacto médio e alto em quase todas as tarefas de planejamento. Isto fortalece a necessidade das empresas tratarem esta tecnologia para a possibilidade de BI com a tarefa de planejamento.Dentro do aspecto de manutenção, os programas Condition Based Maintenance (CBM),segundoGoti et al (2019), podem fornecer vantagens significativas com o objetivo de determinar o ponto potencial de falha, que possibilitará a intervenção preventiva para equipamentos. Um modelo matemático foi desenvolvido pelo autor, sob custo de manutenção corretiva e critério de preventiva, considerando evolução em qualidade e velocidade de produção juntamente com a manutenção corretiva e a manutenção preventiva baseada nas condições.…”