Abstract

This paper presents a method to define the optimal maintenance scope of a production system consisting of multiple k-out-of-n systems connected in series. Maintenance recommendations are based on Remaining Useful Life (RUL) predictions obtained from a Prognostics and Health Management (PHM) system for each production unit within the production system. Defining the techniques applied in order to estimate the degradation level of production units is out of the scope of this paper. It is assumed here that a PHM system is available and provides the degradation level and RUL estimates for each production unit. The goal is to find the maintenance scope that minimizes the expected total cost per cycle until the next maintenance activity. A k-out-of-n load-sharing system is assumed, which means that the failure of a production unit results in a higher load (and consequently a higher degradation rate) on the surviving production units. The total cost comprises the production cost and the maintenance cost. Production cost of each k-out-of-n system is also affected by the number of surviving production units. A preventive maintenance cost is incurred to maintain a degraded but still functional production unit. A corrective maintenance cost is incurredto maintain a failed production unit. An Ant Colony Optimization (ACO) approach is adopted, which allows the proposed method to deal with large instances of the problem. A numerical example is presented to illustrate the application of the proposed method.

Highlights

  • The manufacturing sector is very competitive and the success or failure of companies in this sector is highly influenced by the operational strategies adopted (Heddy et al, 2015)

  • Total cost is broken into production cost and maintenance cost

  • Where L(S) is the expected number of cycles that the production system will operate until it reaches the safety level SL if a maintenance activity with scope S is carried out, M (S) is the total maintenance cost associated with the maintenance activity with scope S and Pj(S) is the expected production cost in the j-th cycle if a maintenance activity with scope S is carried out

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Summary

Introduction

The manufacturing sector is very competitive and the success or failure of companies in this sector is highly influenced by the operational strategies adopted (Heddy et al, 2015). In order to become more competitive, companies in the manufacturing sector are adopting and implementing the Smart. Smart Manufacturing Systems are the integration of advanced technologies to enable the implementation of new processes and increase the efficiency of the existing methods (Weiss et al, 2015). Smart Manufacturing Systems include technologies in a wide range of domains such as automation, decision support, sensing, communication and robotics. The integration of these technologies provides a variety of benefits for companies such as improvements in efficiency and reductions in costs (Weiss et al, 2015), (Bernarden, 2012). Several papers on Smart Manufacturing Systems have been recently published in the literature (Ghonaim, Ghenniwa, & Shen, 2011) (Y. Lu, Morris, & Frechette, 2015) (Brodsky, Krishnamoorthy, Menasce, Shao, & Rachuri, 2014)

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