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This paper aims to present a case study where proportional hazards modeling software is used to evaluate the potential benefits of a condition‐based maintenance policy for military vehicle diesel engines.

Maintenance records for diesel engines were supplied by the UK Ministry of Defence. A proportional hazards model based on these data was created using EXAKT software. Covariate parameters were estimated using the maximum likelihood method and transition probabilities were established using a Markov Chain model. Finally, decision parameters were entered to create an optimal decision model.

Two significant covariates were identified as influencing the hazard rate of the engines. In addition, the optimal decision model indicated a potential economic saving of up to 30 per cent.

A model of this nature is particularly useful to predict failures, improve maintenance policies, and possibly reduce maintenance costs. In addition, the cost of implementing maintenance policies based on this model should be balanced with the potential to reduce the risk of danger to personnel.

The model presented provides military personnel with a decision tool that optimizes the maintenance policy for diesel engines installed in military vehicles.

Presents a case study on overhaul/replacement policy for a fleet of buses owned by a Saudi Arabian transport company. Data on overhaul and operational costs for two types of bus engines have been collected and analysed by standard statistical procedures to establish their functional characteristics and also age dependency of the overhaul costs. Evaluates two major decisions involving overhaul cost limit and number and timings of overhauls. The analysis shows that the existing overhaul policy is similar to the analytically obtained policy.

The purpose of this paper is to focus on an optimizing maintenance policy with repair limit time for a new type of aircraft component, in which the lifetime is assumed to be an uncertain variable due to no historical operation data, and the repair time is a random variable that can be described by the experimental data.

To describe this repair limit time policy over an infinite time horizon, an extended uncertain random renewal reward theorem is firstly proposed based on chance theory, involves uncertain random interarrival times and stochastic rewards. Accordingly, the uncertain random programming model, which minimized the expected maintenance cost rate, is formulated to find the optimal repair limit time.

A numerical example with sensitivity analysis is provided to illustrate the utility of the proposed policy. It provides a useful reference and guidance for aircraft optimization. For maintainers, it plays an important guiding role in engineering practice.

The proposed uncertain random renewal reward process proved useful for the optimization of maintenance strategy with maintenance limited time for a new type of aircraft components, which provides scientific support for aircraft maintenance decision-making for civil aviation enterprises.

The purpose of this paper is to address the issues whether or not minimal repair is effective for a repairable item with a known life distribution and when the minimal repair process should be stopped.

The life restoration degree (LRD) following minimal repair is defined and related to the shape parameter of the distribution so that a choice between minimal and perfect repairs can be made based on the shape parameter. Three replacement policies with minimal repair are considered and the corresponding decision rules are derived to determine when the minimal repair process should be stopped.

Main findings are: first, the LRD of minimal repair is inversely or approximately inversely proportional to the shape parameter, second, the effectiveness of minimal repair increases as the cost ratio of perfect and minimal repairs increases and the shape parameter decreases, and third, the unconditional mean residual life equal the mean time between the first and second failures.

The results can be easily used for maintenance strategy selection and maintenance decision optimization of repairable items.

The control of an inventory where spare parts demand is infrequent has always been difficult to manage because of the randomness of the demand, as well as the existence of a large proportion of zero values in the demand pattern. The purpose of this paper is to propose a just-in-time (JIT) spare parts availability approach by integrating condition monitoring (CM) with spare parts management by means of proportional hazards models (PHM) to eliminate some of the shortcomings of the spare parts demand forecasting methods.

In order to obtain the event data (lifetime) and CM data (first natural frequency) required to build the PHM for the spares demand forecasting, a series of fatigue tests were conducted on a group of turbomachinery blades that were systematically fatigued on an electrodynamic shaker in the laboratory, through base excitation. The process of data generation in the numerical as well as experimental approaches comprised introducing an initial crack in each of the blades and subjecting the blades to base excitation on the shaker and then propagating the crack. The blade fatigue life was estimated from monitoring the first natural frequency of each blade while the crack was propagating. The numerical investigation was performed using the MSC.MARC/2016 software package.

After building the PHM using the data obtained during the fatigue tests, a blending of the PHM with economic considerations allowed determining the optimal risk level, which minimizes the cost. The optimal risk point was then used to estimate the JIT spare parts demand and define a component replacement policy. The outcome from the PHM and economical approach allowed proposing development of an integrated forecasting methodology based not only on failure information, but also on condition information.

The research is simplified by not considering all the elements usually forming part of the spare parts management study, such as lead time, stock holding, etc. This is done to focus the attention on component replacement, so that a just-in-time spare parts availability approach can be implemented. Another feature of the work relates to the decision making using PHM. The approach adopted here does not consider the use of the transition probability matrix as addressed by Jardine and Makis (2013). Instead, a simulation method is used to determine the optimal risk point which minimizes the cost.

This paper presents a way to address some existing shortcomings of traditional spare parts demand forecasting methods, by introducing the PHM as a tool to forecast spare parts demand, not considering the previous demand as is the case for most of the traditional spare parts forecasting methods, but the condition of the parts in operation. In this paper, the blade bending first mode natural frequency is used as the covariate in the PHM in a laboratory experiment. The choice of natural frequency as covariate is justified by its relationship with structural stiffness (and hence damage), as well as being a global parameter that could be measured anywhere on the blade without affecting the results.

Describes the development of two genetic algorithm (GA) programs for cost optimization of opportunity‐based maintenance policies. The combinatorial optimization problem is formulated and it is shown that genetic algorithms are particularly suited to this type of problem. The theoretical basis and operations of a standard genetic algorithm (SGA) are presented with an iterative procedure necessary for implementation of the SGA to least‐cost part replacement. However, an SGA used in an iterative manner may limit the global search capability of the evolutionary computing technique and may lead to suboptimal solutions. To avoid this problem, an improved GA which considers more than two groups simultaneously is devised. This model is based on the permutation representation and genetic sequencing operators originally developed for the travelling salesman problem. The same example used with the SGA confirmed that the improved GA can bring additional savings.

The purpose of this paper is to develop a model that determines whether how much effort of preventive maintenance action is worthwhile for the consumer over the post-sale product life cycle of a repairable complex product where the product is under warranty and subject to stochastic multimode failure process, that is, damaging failure and light failure with different probabilities.

The expected life cycle cost is designed for a warranted product from the consumer perspective. The product failure is quantified with failure rate function, which is the number of failures incurred over the product life cycle. The authors consider the failure rate function reduction method in their model where the scale parameter of a failure rate function is maximized by applying the optimal preventive maintenance level. The scale parameter of any failure distribution refers to the meantime to failure (MTTF). The first-order condition is applied with respect to the maintenance level in order to achieve the convexity of the nonlinear function of the expected life cycle cost function.

The authors have found analytically the close form of the preventive maintenance level, which can be used to find the optimal reduced form of the failure rate function of the product and the minimum product expected life cycle cost under the given condition of multimode stochastic failure process. The authors have suggested different maintenance policies to consumers in order to implement the proposed preventive maintenance model under different conditions. A numerical example further illustrated the analytical model by considering the Weibull distribution.

The consumer may use this study in the accurate modeling of the life cycle cost of a product that is under warranty and fails with a multimode failure process. Also, the suggested preventive maintenance approach of this study helps the consumer in making appropriate maintenance decisions such as to minimize the expected life cycle cost of a product.

This study proposes an accurate estimation of a life cycle cost for a product that is under the support of warranty and fails with multimode. Furthermore, for such a kind of product, which is under warranty and fails with multimode, this study suggests a new preventive maintenance approach that assures the minimum expected life cycle cost.

The purpose of this paper is to propose a novel condition-based maintenance (CBM) policy with two sampling intervals for a system subject to stochastic deterioration described by the Cox’s proportional hazards model (PHM).

In this paper, the new or renewed system is monitored using a longer sampling interval. When the estimated hazard function of the system exceeds a warning limit, the observations are taken more frequently, i.e., the sampling interval changes to a shorter one. Preventive maintenance is performed when either the hazard function exceeds a maintenance threshold or the system age exceeds a pre-determined age. A more expensive corrective maintenance is performed upon system failure. The proposed model is formulated in the semi-Markov decision process (SMDP) framework.

The optimal maintenance policy is found and a computational algorithm based on policy iteration for SMDP is developed to obtain the control thresholds as well as the sampling intervals minimizing the long-run expected average cost per unit time.

A numerical example is presented to illustrate the whole procedure. The newly proposed maintenance policy with two sampling intervals outperforms previously developed maintenance policies using PHM. The paper compares the proposed model with a single sampling interval CBM model and well-known age-based model. Formulas for the conditional reliability function and the mean residual life are also derived for the proposed model. Sensitivity analysis has been performed to study the effect of the changes in the Weibull parameters on the average cost.

The results show that considerable cost savings can be obtained by implementing the maintenance policy developed in this paper.

Unlike the previous CBM policies widely discussed in the literature which use sequential or periodic monitoring, the authors propose a new sampling strategy based on two sampling intervals. From the economic point of view, when the sampling is costly, it is advantageous to monitor the system less frequently when it is in a healthy state and more frequently when it deteriorates and enters the unhealthy state.

The purpose of this paper is to investigate preventive‐repair warranty policies for repairable deteriorating systems using Zhang's geometric process repair model.

The paper aims to establish the importance of preventive repair during warranty. Three cost models have been developed using the average cost rate for the system as the objective function, employing N‐policy for two models therein.

The models have practical applications in warranty cost analysis, as product warranty is an important factor in designing an optimal maintenance policy.

The paper observes that product warranty has not been considered in the study of maintenance policies for repairable deteriorating systems using monotone processes. The numerical example given illustrates that a preventive repair during warranty with N‐policy is preferable compared with a non‐warranted product or a warranted product without preventive repair.

Technological advancements and growing complexity of many real-time systems, namely, communication, transportation, defense systems, etc., necessitate the importance to adopt a well-planned repair process such as phase type quasi-renewal process contributing to an improved system performance. Further, in an attempt to boost the role of maintenance as a financial benefactor, repairman’s multiple vacation policy is incorporated. Also, the significance of the degree of repair is illustrated while indicating the suitability of the matrix-analytic approach via the phase type quasi-renewal operating/repair times in reliability. The paper aims to discuss these issues.

The optimal replacement policy is obtained by employing the matrix-analytic method and minimum average cost rate.

The considered models make a significant contribution towards establishing that the matrix-analytic method, using the phase type quasi-renewal process, aids in reducing the computations and also fills the gap in the literature in the study of deteriorating systems. Availability and rate of occurrence of failures are evaluated in transient and steady-state regime.

This model differs from the existing models, in that, a repairman’s multiple vacation, delayed repair time and representation of the failure occurrence by a mixed Poisson process have been incorporated into the analysis. Also, time-dependent case and N-policy have been adopted to explore the optimality issues using phase type quasi-renewal process analytically. The numerical illustrations warrant that the maintenance policy proposed in this paper produces a considerably lower cost.