Search results

The purpose of this paper is the simultaneous determination of optimal replacement threshold and inspection scheme for a system within condition-based maintenance (CBM) framework.

A proportional hazards model (PHM) is used for risk of failure and a Markovian process to model the system covariates. Total expected long-run cost (including replacement, inspection and downtime costs) is formulated in terms of replacement threshold and inspection scheme. Through an iterative procedure, for all different values of replacement thresholds, their associated optimal inspection scheme is determined using an effective search algorithm. By evaluating the corresponding costs, the optimal replacement threshold and its associated optimal inspection scheme are, then, identified.

The mathematical formulation, that takes into account all different costs, required for the simultaneous determination of optimal replacement threshold and optimal inspection scheme for an item subjected to CBM using PHM is provided. The proposed approach is compared against classical age policy and one state-of-the-art policy through a numerical example. The results show that the proposed approach outperforms other comparing policies.

In practical situations where CBM is implemented, inspections and downtime often incur cost. Under such circumstances, findings of this paper can be utilized for the determination of optimal replacement threshold and optimal inspection scheme so that the CBM cost is minimized.

In most of the reported researches, it is often assumed that inspections have no cost and/or that the time for replacements (either preventive or at failure) is negligible. In the contrary, in this paper the author takes all cost factors including inspection costs, replacement time(s) and their associated downtime costs into account in the simultaneous determination of optimal replacement threshold and optimal inspection scheme.

The purpose of this paper is to determine optimal replacement threshold and optimal inspection interval for an item subjected to condition-based maintenance (CBM). The primarily assumption is that the item's failure replacement cost depends on the item's degradation state at which failure occurs and/or the time the item fails. The cost of inspection is also taken into account.

The control limit replacement policy framework, already reported by some research referred to in this paper, is first extended to include the non-decreasing failure replacement cost assumption. Then, for alternative inspection intervals, replacement thresholds together with their associated total cost including the inspection cost are computed. By comparing the total costs, the optimal inspection interval and its corresponding optimal replacement threshold are simultaneously identified.

The mathematical formulation required for the determination of optimal replacement threshold and optimal inspection interval for an item subjected to CBM under the assumption of non-decreasing failure cost is provided.

In some practical situations where CBM is implemented, the failure replacement cost may depend on the time the failure happens and/or may depend on the system's degradation state. In addition, inspections often incur cost. Under such circumstances, findings of this paper can be utilized for the determination of optimal replacement threshold and optimal inspection interval for the underlying system.

Using the approach proposed in this paper, one could obtain the optimal replacement threshold and the optimal inspection interval for a system subjected to CBM.

The purpose of this paper is to determine a nearly optimal inspection sequence for a series system consisting of two components subject to gradual deterioration and whose failures are not self-announcing and can be detected only through inspection.

The problem is tackled in the context of condition-based maintenance (CBM) with a maintenance model in the class of the control-limit policies for which the maintenance decision is made following inspection by comparison of the deterioration level to critical thresholds. A mathematical model is developed to express the total expected cost per time unit as a function of the inspection instants.

For any given series system composed of two components with known critical deterioration threshold levels, and for any given set of costs related to inspection, inactivity due to failure, and preventive and corrective replacements of each component, a nearly optimal inspection sequence of the system is derived such as the total expected cost is reduced.

Due to the complexity of the cost model with the inspection instants (×1, ×2, ×3, …) being the decision variables, it has not been possible to derive the optimal solution. A quasi-optimal sequence of inspection times is derived along with the corresponding total average cost per time unit.

In many practical situations in which CBM is implemented, a tradeoff between inspection costs and inactivity and replacement costs has to be balanced when determining the intervals between successive inspections at which the degradation level of the components should be assessed and compared to predetermined critical threshold levels. Inspecting too often would increase inspection costs but in the same time it would also increase the probability to avoid a failure and end up with a preventive replacement, whereas not inspecting often enough would increase the probability to end up with a failure increasing replacement and inactivity costs.

While the inspection problem has been largely treated for single component systems, inspection policies become much more complex when considering multi-component systems. A two-component series system is considered in this paper.

This paper aims to address the effect of inspection intervals on cost function in condition‐based maintenance (CBM) and show how selecting an appropriate inspection scheme may reduce the cost associated to a CBM program.

In CBM, replacement policy is often defined as a threshold for replacement or leaving an item in operation until next inspection, depending on monitoring information. The control limit replacement policy framework, already reported by some research referred to in this paper, is utilized to determine the optimal replacement threshold. Having released the assumption that the inspections are performed at fixed and constant intervals, an iterative procedure is proposed to evaluate alternative inspection schemes and their associated total average cost of replacements and inspections.

The paper proposes an approach in which preventive and failure replacement costs as well as inspection cost are taken into account to determine the optimal replacement policy and an age‐based inspection scheme such that the total average cost of replacements and inspections is minimized.

In many practical situations where CBM is implemented, e.g. manufacturing processes, inspections require labor, specific test devices, and sometimes suspension of the operations. Thus, when inspection cost is considerable, by applying the proposed approach, one can obtain an inspection scheme that reduces the cost.

Using the approach proposed in the paper, a cost‐effective age‐based inspection scheme for a system under CBM is determined.

The purpose of this paper is to develop novel preventive maintenance (PM) modeling methods for a cold standby system subject to two types of failures: random failure and deterioration failure.

The system consists of two components and a single repair shop, assuming that the repair shop can only service for one component at a time. Based on semi-Markov theory, transition probabilities between all possible system states are discussed. With the transition probabilities, Markov renewal equations are established at regenerative points. By solving the Markov regenerative equations, the mean time from the initial state to system failure (MTSF) and the steady state availability (SSA) are formulated as two reliability measures for different reliability requirements of systems. The optimal PM policies are obtained when MTSF and SSA are maximized.

The result of simulation experiments verifies that the derived maintenance models are effective. Sensitivity analysis revealed the significant influencing factors for optimal PM policy for cold standby systems when different system reliability indexes (i.e. MTSF and SSA) are considered. Furthermore, the results show that the repair for random failure has a tremendous impact on prolonging the MTSF of cold standby system and PM plays a greater role in promoting the system availability of a cold standby system than it does in prolonging the MTSF of system.

In practical situations, system not only suffers normal deterioration caused by internal factors, but also undergoes random failures influenced by random shocks. Therefore, multiple failure types are needed to be considered in maintenance modeling. The result of the sensitivity analysis has an instructional role in making maintenance decisions by different system reliability indexes (i.e. MTSF and SSA).

This paper presents novel PM modeling methods for a cold standby system subject to two types of failures: random failure and deterioration failure. The sensitivity analysis identifies the significant influencing factors for optimal maintenance policy by different system reliability indexes which are useful for the managers for further decision making.

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.

Despite the existence of multiple asset replacement theories, the economic life replacement method remains a major practical technique for making rational machine replacement decisions. The purpose of this paper is to bridge this method with comprehensive data analytic tools and make it applicable it to modern business reality with abundant data on operating and replacement costs.

This study employs operations research, discrete and continuous optimization, applied mathematical modeling, data analytics, industrial economics and real options theory.

Constructed stochastic algorithms extend the deterministic economic life method and are compared to the contemporary theory of stochastic asset replacement based on real options and dynamic programming. It is proven that both techniques deliver similar results when the cost volatility is small. A major theoretic finding is that the cost uncertainty speeds up the replacement decision.

This research suggests that the proposed stochastic algorithms may become an important tool for managerial decisions about replacement of many similar machines with detailed data on operating and replacement costs.

Compared to the real options replacement theory, major advantages of the proposed algorithms are that they work equally well for any distribution of age-dependent stochastic operating cost. The algorithms are tested on a real industrial case about replacement of medical imaging devices. Numeric simulation supports obtained analytic outcomes.

Considers a system (machine) that is subject to failure (breakdown). Two characterizations are presented. In the first characterization, the state of the system is described by the real age of the machine and the number of failures incurred to date. In the second characterization, the state of the system is described by the real age of the machine and the virtual age of the machine. In either characterization, upon failure, the unit may undergo a repair which can partially reset the failure intensity of the unit. The degree of reset assumed by the repair is a function of the characterization utilized. The other alternative, at a failure, is to conduct a major overhaul that serves to refresh the failure intensity of the unit. General cost structures, depending upon (real age, number of failures) in characterization one or (real age, virtual age) in characterization two are permitted. The decision, on failure to repair or renew is formulated as a discrete semi‐Markov Decision process. Optimal decisions are of the threshold type. The threshold rules depend upon the characterization.

This paper aims to study whether the recycling of a third party competes with the trade-in service of a manufacturer, and explores the optimal trade-in and third-party collection authorization strategies for the manufacturer.

According to whether to authorize a third party to collect its used products, the manufacturer has two choices: one is not authorization (NA); the other is authorization (A). This paper uses profit-maximization model to investigate the optimal decisions of the manufacturer and the third party under NA and A, respectively, and then explores which choice is better for the manufacturer.

It is observed that there is a competition between trade-in service and third-party recycling when the durability parameter of the used product is relatively small. Moreover, when the durability parameter of the used product is relatively large, A is always better choice for the manufacturer; otherwise, NA is a better choice except for the case that the unit trade-in subsidy is low and the salvage of the used product is high.

These results provide managerial insights for the manufacturer and the third party to make decisions in the field of recycling.

This paper is among the first papers to study the competition between trade-in program and third party’s collecting program under government’s trade-in subsidy policy. Moreover, this paper presents the conditions under which the manufacturer should authorize or not authorize the third party to collect its used products.

Wind power is an important source of renewable energy and accounts for significant portions in supplying electricity in many countries and locations. The purpose of this paper is to develop a method for wind power system reliability assessment and condition-based maintenance (CBM) optimization considering both turbine and wind uncertainty. Existing studies on wind power system reliability mostly considered wind uncertainty only and did not account for turbine condition prediction.

Wind power system reliability can be defined as the probability that the generated power meets the demand, which is affected by both wind uncertainty and wind turbine failures. In this paper, a method is developed for wind power system reliability modeling considering wind uncertainty, as well as wind turbine condition through health condition prediction. All wind turbine components are considered. Optimization is performed for maximizing availability or minimizing cost. Optimization is also conducted for minor repair activities to find the optimal number of joint repairs.

The wind turbine condition uncertainty and its prediction are important for wind power system reliability assessment, as well as wind speed uncertainty. Optimal CBM policies can be achieved for optimizing turbine availability or maintenance cost. Optimal preventive maintenance policies can also be achieved for scheduling minor repair activities.

This paper considers uncertainty in both wind speed and turbine conditions and incorporates turbine condition prediction in reliability analysis and CBM optimization. Optimization for minor repair activities is studied to find the optimal number of joint repairs, which was not investigated before. All wind turbine components are considered, and data from the field as well as reported studies are used.