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The subject of investigation reported in this paper is the determination of an optimal replacement time for equipment that deteriorates with time. The following hypothesis is proposed and investigated. While a piece of equipment is in the final stages of its life span, i.e. the wear‐out phase, the application of preventive replacement strategy at constant time intervals reduces total down‐time. The novelty of the approach used in this research lies in the conversion of the more complicated classical constant‐interval replacement model to a simplified but nonetheless effective model. Results are shown for a case where the equipment time‐to‐failure has a normal distribution. These results also hold for a Weibull distribution with known shape and scale parameters. The simplified methods proposed in this paper can assist maintenance managers to better make economic decisions about equipment maintenance.

Wear life of mill liners is an important parameter concerning maintenance decision for mill liners. Variations in process parameters such as different ore properties due to the use of multiple ore types influence the wear life of mill liners whereas random order of processing, processing time and monetary value of different ore types leads to variation in mill profitability. The purpose of the present paper is to develop an economic decision model considering the variations in process parameters and maintenance parameters for making more cost‐effective maintenance decisions.

Correlation studies, experimental results and experience of industry experts are used for wear life modeling whereas simulation is used for maximizing mill profit to develop economic decision model. The weighting approach and simulation have been considered to emphasize the contribution of parameters such as ore value and processing time of a specific ore type to a final result.

A model for estimating lifetime of mill liners has been developed based on ore properties. The lifetime model is combined with a replacement interval model to determine the optimum replacement interval for the mill liners which considers process parameters of multiple ore types. The finding of the combined model results leads to a significant improvement in mill profit. The proposed combined model also shows that an optimum maintenance policy can not only reduce the downtime costs, but also affect the process performance, which leads to significant improvement in the savings of the ore dressing mill.

The proposed economic decision model is practically feasible and can be implemented within the ore dressing mill industries. Using the model, the cost‐effective maintenance decision can increase the profit of the organization significantly.

The novelty is that the new combined model is applicable and useful in replacement decision making for grinding mill liners, in complex environment, e.g. processing multiple ore types, different monetary value of the ore type and random order of ore processing.

This paper seeks to develop and present a new mathematical formulation to determine the optimal preventive maintenance and replacement schedule of a system.

The paper divides the maintenance‐planning horizon into discrete and equally‐sized intervals and in each period decide on one of three possible actions: maintain the system, replace the system, or do nothing. Each decision carries a specific cost and affects the failure pattern of the system. The paper models the cases of minimizing total cost subject to a constraint on system reliability, and maximizing the system reliability subject to a budgetary constraint on total cost. The paper presents a new mathematical function to model an improvement factor based on the ratio of maintenance and repair costs, and show how it outperforms fixed improvement factor models by analyzing the effectiveness in terms of cost and reliability of the system.

Optimal decisions in each period over a planning horizon are sought such that the objectives and the requirements of the system can be achieved.

The developed mathematical models for this improvement factor can be used in theoretical and practical situations.

The presented models are effective decision tools that find the optimal solution of the preventive maintenance and replacement scheduling problem.

Service systems that require failure rate below a predetermined value usually need maintenance in order to operate at that level of reliability. One major issue in maintenance systems is the optimal maintenance schedule that incurs minimum total cost. Considering fixed inflation rate and failure rate variation after maintenance, an algorithm of balanced maintenance scheduling is developed. The algorithm provides an optimal number of replacement and preventive maintenance to minimize the total maintenance cost over a certain planning period. A numerical example is demonstrated and compared with the results reported by other researchers.

Prediction of the duration of the downtime caused by maintenance, especially in the cases where the system considered consists of several repairable items, presents a challenge for maintenance managers, because of possible revenue losses during these intervals of time. Responds to this challenge through the new methodology for the fast, accurate prediction of maintainability measures related to the group replacement maintenance policy. It is applicable to group maintenance tasks in which individual replacement tasks are performed: simultaneously, sequentially, and combined. The method presented could be successfully used at the planning stage of the operations/production process when the information available is based on previous experience only, as well as at the stage when the process is performed. The applicability and usefulness of the methodology proposed is demonstrated through an illustrative numerical example.

The study aims to design the limited number of random working cycle as a warranty term and propose two types of warranties, which can help manufacturers to ensure the product reliability during the warranty period. By extending the proposed warranty to the consumer's post-warranty maintenance model, besides the authors investigate two kinds of random maintenance policies to sustain the post-warranty reliability, i.e. random replacement first and random replacement last. By integrating depreciation expense depending on working time, the cost rate is constructed for each random maintenance policy and some special cases are provided by discussing parameters in cost rates. Finally, sensitivities on both the proposed warranty and random maintenance policies are analyzed in numerical experiments.

The working cycle of products can be monitored by advanced sensors and measuring technologies. By monitoring the working cycle, manufacturers can design warranty policies to ensure product reliability performance and consumers can model the post-warranty maintenance to sustain the post-warranty reliability. In this article, the authors design a limited number of random working cycles as a warranty term and propose two types of warranties, which can help manufacturers to ensure the product reliability performance during the warranty period. By extending a proposed warranty to the consumer's post-warranty maintenance model, the authors investigate two kinds of random replacement policies to sustain the post-warranty reliability, i.e. random replacement first and random replacement last. By integrating a depreciation expense depending on working time, the cost rate is constructed for each random replacement and some special cases are provided by discussing parameters in the cost rate. Finally, sensitivities to both the proposed warranties and random replacements are analyzed in numerical experiments.

It is shown that the manufacturer can control the warranty cost by limiting number of random working cycle. For the consumer, when the number of random working cycle is designed as a greater warranty limit, the cost rate can be reduced while the post-warranty period can't be lengthened.

The contribution of this article can be highlighted in two key aspects: (1) the authors investigate early warranties to ensure reliability performance of the product which executes successively projects at random working cycles; (2) by integrating random working cycles into the post-warranty period, the authors is the first to investigate random maintenance policy to sustain the post-warranty reliability from the consumer's perspective, which seldom appears in the existing literature.

This paper develops a framework for selecting the most efficient and effective preventive maintenance policy using multiple-criteria decision making and multi-objective optimization.

The critical component is identified with a list of maintenance policies, and then its failure data are collected and the optimization objective functions are defined. Fuzzy AHP is used to prioritize each objective based on the experts' questionnaire. Weighted comprehensive criterion method is used to solve the multi-objective models for each policy. Finally, the effectiveness and efficiency are calculated to select the best maintenance policy.

For a fleet of buses in hot climate environment where coolant pump is identified as the most critical component, it was found that block-GAN policy is the most efficient and effective one with a 10.24% of cost saving and 0.34 expected number of failures per cycle compared to age policy and block-BAO policy.

Only three maintenance policies are compared and studied. Other maintenance policies can also be considered in future.

The proposed methodology is implemented in UAE for selecting a maintenance scheme for a critical component in a fleet of buses. It can be validated later in other Gulf countries.

This research lays a solid foundation for selecting the most efficient and effective preventive maintenance policy for different applications and sectors using MCDM and multi-objective optimization to improve reliability and avoid economic loss.

The main purpose of present study is to model the replacement policy under uncertainty for managerial application based on grey-reliability approach by considering the subjective views of quality control circle (QCC). The study objectively links the optimality between individual replacement and group replacement policies for determining the minimum operational costs. The integrated framework between QCC, replacement theory, grey set theory and supply chain management is presented to plan replacement actions under uncertainty.

The study proposes the concept of grey-reliability index and built a decision support model, which can deal with the imprecise information for determining the minimum operational costs to plan subsequent maintenance efforts.

The findings of the study establish the synergy between individual replacement and group replacement policies. The computations related to the numbers of failures, operational costs, reliability index and failure probabilities are presented under developed framework. An integrated framework to facilitate the managers in deciding the replacement policy based on operational time towards concerning replacement of assets that do not deteriorate, but fails suddenly over time is presented. The conceptual model is explained with a numerical procedure to illustrate the significance of the proposed approach.

A conceptual model under the framework of such items, whose failures cannot be corrected by repair actions, but can only be set by replacement is presented. The study provides an important knowledge based decision support framework for crafting a replacement model using grey set theory. The study captured subjective information to build decision model in the ambit of replacement.

The paper seeks to ease the implementation of predictive maintenance policy in industry using the root cause analysis technique, and to compare the reliability and cost effectiveness of root cause based maintenance (RCBM) relative to conventional corrective maintenance (CM).

The system is modularized into its components and maintenance schedules are developed based on each component's individual degradation trends. The effectiveness of RCBM over CM is studied by analyzing system reliability patterns and total maintenance cost functions obtained through empirical cost models, accounting for yield and production loss, maintenance, replacement and catastrophic failure costs. Cost variations for various possible failure distribution parameter values (β, η) under the CM and RCBM policies are also obtained. The proposed methodology is tested in a real aircraft failures case study.

RCBM is generally more effective over CM in achieving timely maintenance at optimal cost (savings up to 65 percent) while keeping high system reliability, for a wide range of (β, η) values. However, CM could still be beneficial for a restricted range of large (β, η).

Industry should consider shifting from CM to adopt the proposed RCBM policy, which is proved to be more efficient in most cases. The implementation is not necessarily complex.

The effectiveness of RCBM over CM in terms of reliability and cost considerations is clearly illustrated. This paper justifies the need to shift from CM to RCBM, which brings us closer to a practical implementation of predictive maintenance. This work also serves as a simple and valuable guide to implementation for maintenance and operational managers in production industries.

States that the concept of condition‐based maintenance (CBM) has been widely accepted in practice since it enables maintenance decisions to be made based on the current state of equipment. Existing CBM methods, however, mainly rely on the inspector’s experience to interpret data on the state of equipment, and this interpretation is not always reliable. Aims to present a preventive maintenance policy based on inspections and a proportional hazards modelling approach with time‐dependent covariates to analyse failure‐time data statistically. Presents the structure of the software, currently under develop‐ ment and supported by the CBM Project Consortium.