摘要：In reliability and maintenance engineering, predictive methods play a major role for estimating the remaining useful life (RUL) of equipment. However, in the most cases the RUL estimation is based on two types of approaches: model-based solution and data-driven solution. Data-driven solution is a very realistic solution, but requires the availability of large quantity of collected data using sensors networks, big storage capacity, supercomputers for processing, and high-level algorithms such as machines learning, convolutional Neural Networks, Hidden Markov Models. While model-based solution methods are less difficult to deploy, they use techniques based on the mean residual life (MRL) value. This paper proposes an integrated decision support model for minimizing the maintenance expected cost, and for reducing the excess of spare-parts usage of multi-component production systems. This decision support model includes three performance indicators that are: the renewal function (RF), the mean residual lifetime (MRL) and the renewal mean residual lifetime (RMLR). The contribution of this research consists of (1) introducing the MRL function for a class of useful probability distributions, (2) proposing the Renewal MRL (RMRL) as a predictive maintenance strategy, (3) applying the approach-MRL to maintenance scheduling problems, (4) dressing a comparative numerical study between the different proposed models using an industrial case study. Besides, the comparative study performing the RF, MRL, and RMRL is carried out using basic and modified replacement strategies consisting of an age replacement policy (ARP) and an opportunistic maintenance solution. In addition, a case study from an Electrical power system is proposed by providing numerical results that discuss and illustrate the outcome gains in terms of maintenance costs saving and spare parts replacements’ minimization. However, the proposed solution is demonstrated to have a positive effect to enhance sustainable development systems, and it is provided soon to cover other theoretical and application aspects of the MRL.
