Total Cost of Ownership Driven Methodology for Predictive Maintenance Implementation in Industrial Plants

This paper proposes a methodology to drive from a strategic point of view the implementation of a predictive maintenance policy within an industrial plant. The methodology integrates the evaluation of system performances, used to identify the critical components, with simulation and cost analysis. The goal is to evaluate predictive maintenance implementation scenarios based on alternative condition monitoring (CM) solutions, under the lenses of Total Cost of Ownership (TCO). This allows guiding the decision on where in the industrial system to install diagnostic solutions for monitoring of asset health, by keeping a systemic and life cycle-oriented perspective. Technical systemic performances are evaluated through Monte Carlo simulation based on the Reliability Block Diagram (RBD) model of the system. To validate the methodology, an application case study focused on a production line of a relevant Italian company in the food sector is presented. © IFIP International Federation for Information Processing 2019.