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Operational Availability vs. Time-50% Lifetime SimultaneousReplacement Frequency Instantaneous operational availability with a platform simultaneous-replacement frequency of 91.25 days (50% of the lifetime of the critical parts that make up the platform) for a system of 300 platforms as a function of mission time.
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Achieving high reliability is one of the major objectives in the development of the future combat system (FCS) family of military vehicles. The proposed solution to achieve this objective is a prognostics-based approach characterized by a capability to monitor the status of mission-critical components and forecast the future state of the FCS system...
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Citations
... Macheret et al. [27] applied methods based on probabilistic dependencies and Monte Carlo simulations to study the reliability of military vehicles. A resulting exponential model was satisfactory in describing the time between failures (TBF). ...
... Models based on Birnbaum importance measure 2-, 3-and 5-component systems [31] Probabilistic methods and Monte Carlo simulation Exponential model Military vehicles [27] Probabilistic and dispersion methods Reliability models including hard and soft failures Multi-state systems [26] Bayesian methods Combined model of component reliability Multi-component complex system [14] Markov processes 3-state model Power supply systems in transport telematics devices [47] 4-state model Bearings [22] 5-state model Micro-electro-mechanical systems [54] 2-, 4-and 8-state model Port distribution power system [12] 10-state model GPS Receivers [41] Semi-Markov processes ...
... whereas the matrix elements are calculated according to the dependencies [52] (22)(23)(24)(25)(26)(27)(28)(29)(30): ...
Highlights Abstract ▪ The 3-state model has been developed for reliability modelling. ▪ Based on the semi-Markov model, the reliability characteristics were calculated. ▪ Readiness and suitability indicators were used to assess the operational process. ▪ The results of the 3-state model have been compared with those of the 9-state model. Vehicles are important elements of military transport systems. Semi-Markov processes, owing to the generic assumption form, are a useful tool for modelling the operation process of numerous technical objects and systems. The suggested approach is an extension of existing stochastic methods employed for a wide spectrum of technical objects; however, research on light utility vehicles complements the subject gap in the scientific literature. This research paper discusses the 3-state semi-Markov model implemented for the purposes of developing reliability analyses. Based on an empirical course of the operation process, the model was validated in terms of determining the conditional probabilities of interstate transitions for an embedded Markov chain, as well as parameters of time distribution functions. The Laplace transform was used to determine the reliability function, the failure probability density function, the failure intensity, and the expected time to failure. The readiness index values were calculated on ergodic probabilities. Keywords This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/) semi-Markov model, reliability modelling, readiness, maintenance analysis, transportation system.
... A novel method for evaluating the reliability of renewable objects is proposed in [5]; the authors combined Monte Carlo methods with fault trees. In relation to the field of readiness and reliability of military structures, the authors of [34] conducted Monte Carlo simulations for three strategies for managing the platform operation processes, namely, replacing as needed, re-inspection at a specified interval, and prognostics. The prognostic approach optimised the operational readiness of military equipment by forecasting damage and reducing logistics delays. ...
This research paper presents studies on the operation process of the Honker 2000 light utility vehicles that are part of the Polish Armed Forces transport system. The phase space of the process was identified based on the assumption that at any given moment the vehicle remains in one of four states, namely, task execution, awaiting a transport task, periodic maintenance, or repair. Vehicle functional readiness and technical suitability indices were adopted as performance measures for the technical system. A simulation model based on Monte Carlo methods was developed to determine the changes in the operational states. The occurrence of the periodic maintenance state is strictly determined by a planned and preventive strategy of operation applied within the analysed system. Other states are implementations of stochastic processes. The original source code was developed in the MATLAB environment to implement the model. Based on estimated proba-bilistic characteristics, the authors validated 16 simulation models resulting from all possible cumulative distribution functions (CDFs) that satisfied the condition of a proper match to empirical data. Based on the simulated operation process for a sample of 19 vehicles over the assumed 20-year forecast horizon, it was possible to determine the functional readiness and technical suitability indices. The relative differences between the results of all simulation models and the results obtained through the semi-Markov model did not exceed 6%. The best-fit model was subjected to sensitivity analysis in terms of the dependence between functional readiness and technical suitability indices on vehicle operation intensity. As a result, the proposed simulation system based on Monte Carlo methods turned out to be a useful tool in analysing the current operation process of means of transport in terms of forecasts related to a current environment, as well as when attempting its extrapolation.
... One way to handle the idle time is to consider the idle time as uptime (operational time), as suggested in 16 . Commonly, one can consider the operation period as a year. ...
... On the other hand, warships in a navy differ from the airborne weapon systems, since the naval ships operate without supplying on the sea after leaving the seaport 16 . Also it can be constructed another K-factor ( K ′ ′ ) as Eqn. ...
As the complexity of weapon systems has grown exponentially during the past few years, initial operation capability has been a crucial factor for military forces. Concurrent spare parts (CSPs) is the quantity of spare parts ensuring initial operating period specified by demanding forces acquiring newly deployed weapon systems. Because of the growth of system complexity, recommending precise CSP is not trivial. The Republic of Korea developed an improved CSP recommendation system and deployed the system for naval weapon systems. In this paper, we increase the prediction accuracy of CSP up to 23.1 per cent and 7.16 per cent higher in terms of budget constraint and operational availability (Ao) constraint. The main improvement is achieved by facilitating simulations using the real field data from Korean air force. Also, we propose two validation approaches and show the possibility of extension to the general weapon systems. From the experimental study, we show that the CSP recommendation system can be deployed for navy and air forces.
... These factors are called Downtime Influence Factors (DIFs)as described in Alshafiq et al. [8]. Most studies are limited to a single factor such as obsolescence or spares availability in Sandborn [9] and Koehn et al. [10], or two or three factors at most. Therefore, new knowledge could be gained if DIFs are studied holistically. ...
Operational availability of naval ships, which reflects the number of days they are available for operational tasking in a year, is a complex problem. The number of days the ships are able to spend in an area of operations reveals the sustainability of the naval force in showing of presence and deterrent capability. There have been numerous literatures on calculating downtime through Mean Time between Failure (MTBF) and Mean Time to Repair (MTTR) to obtain availability value; however there have been limited literatures pinpointing to the root cause of the various downtime, called Downtime Influence Factors (DIF) for naval vessels. The limited literatures on DIFs of naval vessels are further restricted in the study of a single factor such as obsolescence or spares availability, or two or three factors at most, whilst in reality the DIFs encompasses a wide range of human and equipment related factors that most researchers have not attempted to study. The situation is further complicated by issues of equipment and component redundancies as well as possible interdependencies between each DIFs. The current research uses a five-stage sequential modified Delphi approach including risk analysis and snowballing technique to identify, validate and rank the severity of all DIFs from two sets of experts in naval ship maintenance contracts. The study revealed 15 severe DIFs involving human and equipment related factors impacting naval ship availability. The result complemented and validated the findings of previous study by the authors involving 30 experts. The results enable the navies and supporting industries to focus on pinpointed areas of concern to enable them to increase the operational availabilities of their ships in the fleet.
... In presented index, the availability is taken into consideration as the operational availability. Operational availability is ratio of time of the equipment available for the operation to the total operational time [20]. Availability is calculated by Eq. (1) [21]: ...
Although mining production depends on various equipments, significant amount of production loss can be attributed a specific equipment or fleet. Bottleneck is defined not only by production loss but also by our satisfaction from the equipment. The user satisfaction could be measured as machine effectiveness. Mining literatures on performance improvement and optimization of equipment operations assert importance of availability, utilization and production performance as key parameters. These three parameters are useful for evaluating effectiveness of equipment. Mine production index (MPI), which can represent the effect of these factors, has been applied for continuous operation in mining. MPI uses Fuzzy Delphi Analytical Hierarchy Process to determine importance of each three parameter for individual equipment. A case study in a Swedish open pit mine was done to evaluate the field application of MPI. The results reveal that crusher is the bottleneck equipment in studied mine. As a methodical approach, an algorithm which uses MPI and detects bottleneck in continuous mining operation has been proposed.
... The equations and methodologies given in this paper describe the most common techniques, to determine the OA, as well as their limitations and shortcomings. From another side, an investigation study on the effects of the prognostics capability on OA of military land systems has been conducted in Koehn et al. (2005). It is also shown in that paper that OA of military vehicles is significantly affected by Administrative Logistics Delay Time (ALDT) and repair times. ...
This paper presents a simulation study for military land equipment operational availability (OA). A discrete event simulation model was developed within this study to support the analysis of the effect of several maintenance regimes on OA namely Corrective Maintenance (CM), Scheduled Preventive Maintenance (SPM), and Condition-Based Maintenance (CBM). The model was implemented using the ARENA environment and simulations were run with various probability distributions for the various process durations such as task duration, time between failure, repair time, supply delay and non-operating time. The maintenance "cost" metric has been considered in this model. The results indicate that the simulation model capture the important trends of an OA such as military land systems for the CM, SPM and CBM regimes. The simulations results showed that as time between repairs becomes larger, the SPM triggers less often, so CBM catches more failures. The results showed also that the way the three maintenance regimes (CM, CBM, and SPM) interact can be complex, particularly because parts can fail during a task. Many possible improvements were identified, at the design, implementation, and results levels. © ECMS Valeri M. Mladenov, Petia Georgieva, Grisha Spasov, Galidiya Petrova (Editors).
... With regard to variables involved in modeling, it can be first assumed that failure repair time m t , relaxation time d t and delay time s t are all random variables. Among them, m t and s t are subject to exponential distribution [8,9] (whose mean values are MTTR and MLDT respectively), while d t can be arranged to follow exponential distribution (whose mean value is MRT) or normal distribution [10]. ...
The definition and calculation of the operational readiness and availability of weapon system have been described. The relationship and difference between them have been analyzed. On the assumption that the repair time, logistic delay time and the relaxation time are subject to different distributions, the models and reasoning of combinations of different distribution and modeling ideas were carried out, the reasonability and typical numerical calculations have been presented according to the modeling results and input data. The trade-offs analyses between the reliability, maintainability and testability indicators based on the modeling results are briefly discussed, and several enlightening conclusions are obtained.
... However, this analysis is focused on the operational availability since it implicitly incorporates other forms of downtime-based availability and it is the most commonly used form of availability specified in availability contracts, however, the proposed design for availability methodology is general and could be easily extended to incorporate other types of availability. Operational availability is the probability that a system or piece of equipment operates ordinarily, i.e., functional and available for operation when requested, over a specific period of time under stated conditions [2,3]. Operational availability (A O ) accounts for all types of maintenance and logistics downtimes. ...
"Availability-based" contracting originated because customers with high availability requirements are in many cases interested in migrating from buying the actual system to buying the availability of the system. A well-known example of availability-based contracting is Performance Based Logistics (PBL). Prognostics and Health Management (PHM) methods are incorporated into systems to avoid unanticipated failures that can potentially impact system safety, result in additional life cycle cost, and/or adversely affect the system availability. While predicting the availability of a system based on known or predicted system parameters is relatively straightforward and can be accomplished using existing methods; determining the system parameters that result in a desired availability is not and is generally performed using "brute force" search-based methods that become quickly impractical for designing systems with more than a few variables and when uncertainties are present. This paper presents the application of PHM within a "design for availability" approach that uses an availability requirement to predict the required logistics, design (including reliability) and operation parameters with and without the application of PHM methods. A life cycle cost analysis is used to quantify trade-offs of using PHM methods versus more traditional maintenance approaches in the context of availability contracts.
