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In this study, we employed a geometric process approach to resolve gearbox maintenance problems. The approach is realistic and direct in modelling the characteristics of a deteriorating system such as a gearbox since a decreasing geometric process can model a gearbox’s successive operating times and an increasing geometric process can model the corresponding consecutive repair times. First, two test statistics were used to check whether the process was geometric or not. Next, model parameters of the geometric process were estimated using the simple linear regression techniques. Finally, the optimal replacement policy based on minimising the long‐run average cost per day was determined for each type of gearbox.

In this paper, we consider a repair‐time limit replacement problem with imperfect repair and develop a graphical method to determine the optimal repair‐time limit which minimizes the expected total discounted cost over an infinite time horizon. The method proposed can be applied to an estimation problem of the optimal repair‐time limit from the empirical repair‐time data. Then, the modified scaled total time on test transform of the underlying repair‐time distribution function is used. Numerical examples are devoted to examine asymptotic properties of the nonparametric estimator for the optimal repair‐time limit.

A temporal XML database could become an inconsistent model of the represented reality after a retroactive update. Such an inconsistency state must be repaired by performing corrective actions (e.g. payment of arrears after a retroactive salary increase) either immediately (i.e. at inconsistency detection time) or in a deferred manner, at one or several chosen repair times according to application requirements. The purpose of this work is to deal with deferred and multi-step repair of detected data inconsistencies.

A general approach for deferred and stepwise repair of inconsistencies that result from retroactive updates of currency data (e.g. the salary of an employee) in a valid-time or bitemporal XML database is proposed. The approach separates the inconsistency repairs from the inconsistency detection phase and deals with the execution of corrective actions, which also take into account enterprise’s business rules that define some relationships between data.

Algorithms, methods and support data structures for deferred and multi-step inconsistency repair of currency data are presented. The feasibility of the approach has been shown through the development and testing of a system prototype, named Deferred-Repair Manager.

The proposed approach implements a new general and flexible strategy for repairing detected inconsistencies in a deferred manner and possibly in multiple steps, according to varying user’s requirements and to specifications which are customary in the real world.

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.

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.

The internet of things and just-in-time are the embryonic model of innovation for the state-of-the-art design of the service system. This paper aims to develop a fault-tolerant machining system with active and standby redundancy. The availability of the fault-tolerant redundant repairable system is a key concern in the successful deployment of the service system.

In this paper, the authors cogitate a fault-tolerant redundant repairable system of finite working units along with warm standby unit provisioning. Working unit and standby unit are susceptible to random failures, which interrupt the quality-of-service. The system is also prone to common cause failure, which tends its catastrophe. The instantaneous repair of failed unit guarantees the increase in the availability of the unit/system. The time-to-repair by the single service facility for the failed unit follows the arbitrary distribution. For increasing the practicability of the studied model, the authors have also incorporated real-time machining practices such as imperfect coverage of the failure of units, switching failure of standby unit, common cause failure, reboot delay, switch over delay, etc.

For deriving the explicit expression for steady-state probabilities of the system, the authors use a supplementary variable technique for which the only required input is the Laplace–Stieltjes transform (LST) of the repair time distribution.

For complex and multi-parameters distribution of repair time, derivation of performance measures is not possible. The authors prefer numerical simulation because of its importance in the application for real-time uses.

The stepwise recursive procedure, illustrative examples, and numerical results have been presented for the diverse category of repair time distribution: exponential (M), n-stage Erlang (Ern), deterministic (D), uniform (U(a,b)), n-stage generalized Erlang (GE_[n]) and hyperexponential (HE_[n]).

Concluding remarks and future scopes have also been included. The studied fault-tolerant redundant repairable system is suitable for reliability analysis of a computer system, communication system, manufacturing system, software reliability, service system, etc.

As per the survey in literature, no previous published paper is presented with so wide range of repair time distribution in the machine repair problem. This paper is valuable for system design for reliability analysis of the fault-tolerant redundant repairable.

As the intensity of natural disasters increases, there is a need to develop policies and procedures to assist various agencies with moving aid to affected areas. One of the biggest limitations to this process is damage to transportation networks, in particular waterways. To keep waterways safe, aids to navigation (ATONs) are placed in various areas to guide mariners and ships to their final destination. If the ATONs are damaged, then the waterways are left unsafe, making it difficult to move supplies and recover from a disaster. The aim of this paper is to explore the effectiveness of pre‐positioning strategies for port recovery in response to a natural disaster.

A stochastic facility location model is presented to determine where teams and commodities should be pre‐positioned in order to maximize the number of ATONs repaired, given a constraint on response time. The first stage decisions focus on determining the location of resources. The second stage decisions consist of the distribution of supplies and teams to affected areas.

Results show the benefit of pre‐positioning and the value of coordination toward the responsiveness of restoring waterways. Furthermore, the relationship between resources, repair time, and response is characterized.

There has been extensive work addressing pre‐positioning as it relates to responding to the needs of populations affected by disasters. However, little has been done to explore pre‐positioning in the context of business recovery from severe weather events.

The purpose of this paper is to investigate performance times from a quality engineering perspective for response and repair of pipes at a public utility. The objective is to scientifically determine the pipe that offers the most desirable downtime (DT) and time to repair (TTR).

Four types of water supply pipes – concrete cylinder (CC), cast iron (CI), ductile iron (DI), and polyvinyl chloride (PVC) – in prevalent use at the City and County of Honolulu Board of Water Supply were analyzed to determine the pipe type that is most consistently repaired to desired performance specifications. Data for mean downtime (MDT) and mean time to repair (MTTR) were used to evaluate the stability and capability of the repair processes for each pipe type. The analysis was completed through the use of control charts, operating characteristic (OC) curves, and process capability indices.

The results of the analysis indicated that CI pipes were the worst material in terms of DT and TTR. The control charts for MDT for all pipe types, and the MTTR for CI and CC pipes, were found to be out of statistical control, but the control charts for the MTTR of DI and PVC pipes were discovered to be in control. According to the OC curves, in which the hypothesis stated that the average MDT or MTTR was between the specification limits, there was a high tendency in all pipe types to accept the hypothesis when it was true. However, the probability of type I errors was high from operational standards at the USL level. Process capability analyses found that only CC pipes were able to meet performance design specifications; however, repair times are extremely large for CC pipes. Overall, it is recommended that CI pipes be replaced when the opportunity arises.

This investigation serves to address a major query in asset management at the public utility, that of which pipes should be selected during design and procurement from a maintenance perspective. In addition, the study helps to understand the trend of DT and TTR for the various pipes.

Quality water supply is of paramount social importance in modern cities.

A quality engineering approach to asset management for pipe systems at public utilities that serves to add a new dimension to asset performance analysis is adopted.

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.

Availability of military systems is of major concern for military planners at both tactical (battle) level and at strategic level (long‐term national planning). Availability factors critically affect the operational effectiveness during military operations. Military systems are complex and lend themselves to simulation approach for availability estimation as analytical solutions are extremely difficult. The purpose of this paper is to discuss the method of systems modeling to approach the simulation for availability estimation of military systems.

Availability measures are needed for two main domains of application: peacetime operations and battlefield situations. Availability measures include not only inherent availability of interest to designers/manufacturers, but also operational availability and field/service availability. The simulation approach adopted here involves discrete event simulation (DES) techniques using Monte Carlo methods since a network of events can be included in the model. A system engineering approach is emphasized, starting with system representation and characterisation, and using system aggregation techniques.

Modeling involves hierarchical models and network diagrams for events. First the system is described by a hierarchical model; the events and transitions are represented with state transition diagrams (STD). The simulation scheme would be based on initial resources or inventory as military operations proceed, with random variates for event times or rates. The availability as a function of time A(t) is arrived at. The reliability and maintainability models are simulated with probability distributions or using empirical distributions. The methods of data collection and analysis, and sensitivity analysis are mentioned. The methodology is explained with two case studies from the authors' work. The approaches of other workers in recent years are summarised.

The paper shows that the simulation models can suitably be modified to include their applications for army and navy military operations. Also, with proper data on all major subsystems of interest for the weapon platform and accurate past war data, it is possible to fine‐tune the models for online use during military campaigns. The availability figures thus obtained may also be used for procurement decisions for long‐term and strategic planning.