Complex Repairable Systems Research Articles

Performance analysis of a complex repairable system with two subsystems in series configuration with an imperfect switch

This paper presents the study of reliability measures of a complex system consisting of two subsystems, subsystem-1, and subsystem-2, in a series configuration with switching device. The subsystem-1 has five units that are working under 2-out-of-5: G policy and the subsystem-2 has two units that are working under 1-out-of-2: G policy. Moreover, the switching device in the system is unreliable, and as long as the switch fails, the whole system fails immediately. Failure rates of units of subsystems are constant and assumed to follow the exponential distribution. Still, their repair supports two types of distribution, namely general distribution and Gumbel-Hougaard family copula distribution. Using the supplementary variable technique, Laplace transformations, and copula approach differential equations developed. Important reliability characteristics such as availability of the system, reliability of the system, MTTF, profit analysis, and sensitivity analysis for MTTF have computed for fixed values of failure and repair rates. Particular cases corresponding to the switching device have also considered. Graphs demonstrate results, and consequently, conclusions have done.

Stochastic analysis of a two units’ complex repairable system with switch and human failure using copula approach

This paper deals with the study of a complex system which consists of two subsystems in a series configuration (subsystem 1 and subsystem 2). The subsystem 2 is connected to subsystem 1 in a series configuration. The subsystem 1 has three identical units in parallel configuration and working under 2-out-of-3: G policy and its units are controlled by an instantaneous change over switch. Subsystem 2 has two identical units and uses 1-out-of-2: G scheme. A human operator operates the system, and human failure is considered to damage the entire system completely. All failure rates are assumed to follow exponential distribution and repairs follow two types of distribution (general distribution and Gumbel–Hougaard family copula) distribution. The system is analyzed using Supplementary variables technique, and the various measures of reliability have discussed. The computations for availability, reliability, MTTF, and profit benefit by operation of the system have done for different values of failure and repair rates. Graphs demonstrate results, and consequently, conclusions have done.

Cost assessment of complex repairable system consisting two subsystems in series configuration using Gumbel Hougaard family copula

Purpose The purpose of this paper is to covenant with the cost assessment of a complex repairable system, consisting of two subsystems (Subsystem 1 and Subsystem 2) connected in series configuration and being operated by a human operator. Each subsystem has two identical units in parallel configuration and has different types of failure and two types of repairs (general repair and copula repair). Through the transition diagram, the system of first-order partial differential equations is derived and solved using a supplementary variable technique, Laplace transforms. All failures are assumed to follow exponential distribution, whereas repairs follow two types of distributions that are general and Gumbel–Hougaard family copula. In this paper, explicit expressions for reliability, availability, mean time to failure (MTTF) and cost analysis functions have been obtained. In this paper, two types of repairs (copula repair and general repair) have been studied, and it has been concluded that copula repair is more reliable as compared to general repair. Some computations are taken as particular case by evaluating: reliability, availability, MTTF and cost analysis, so as to capture the effect of both failure and repair rates to reliability measures. The results have been shown in tables and graphs. The convincing part has been discussed in last section of this study. Design/methodology/approach This paper is focused on the cost assessment of a system consisting two subsystem series configuration. Each subsystem has two identical units in parallel configuration. The performance of the system has been analyzed by supplementary variable techniques and Laplace transforms. Various measures of the reliability have been discussed by evaluations. Software called Maple 13 is used for computations. Findings In this research paper, the authors have evaluated the operational cost and incurred profit of the system together with other reliability measures for various situations and different types of failures and two types of repairs using Gumbel–Hougaard family copula distribution. Research limitations/implications The present research focuses on the series and parallel configured complex systems that is used everywhere in industry and other sectors. The authors main aim is to claim that repair through the joint probability distribution copula is far better than general repair. Copula repair for a completely failed system is more beneficial for industrial system operations that will increase profit to the industrial sector. Practical implications The authors have observed that when repair follows general distribution the values of reliability obtained of the system are less compared to the those obtained when the authors apply copula repair, a joint probability distribution. It is a clear implication for industrial sector and organization to use the policy for a better generate revenue. Social implications According to the best of authors’ knowledge, there is no social implication as this study is meant for reliability section. The study in management and case study matters is considered to have social implication. Originality/value This research is the original work of authors. Nothing has been copied from any paper or book. The references are cited according to the relevance of study.

Performance modeling and optimization for complex repairable system of paint manufacturing unit using a hybrid BFO-PSO algorithm

PurposeThe purpose of this paper is to optimize the performance for complex repairable system of paint manufacturing unit using a new hybrid bacterial foraging and particle swarm optimization (BFO-PSO) evolutionary algorithm. For this, a performance model is developed with an objective to analyze the system availability.Design/methodology/approachIn this paper, a Markov process-based performance model is put forward for system availability estimation. The differential equations associated with the performance model are developed assuming that the failure and repair rate parameters of each sub-system are constant and follow the exponential distribution. The long-run availability expression for the system has been derived using normalizing condition. This mathematical framework is utilized for developing an optimization model in MATLAB 15 and solved through BFO-PSO and basic particle swarm optimization (PSO) evolutionary algorithms coded in the light of applicability. In this analysis, the optimal input parameters are determined for better system performance.FindingsIn the present study, the sensitivity analysis for various sub-systems is carried out in a more consistent manner in terms of the effect on system availability. The optimal failure and repair rate parameters are obtained by solving the performance optimization model through the proposed hybrid BFO-PSO algorithm and hence improved system availability. Further, the results obtained through the proposed evolutionary algorithm are compared with the PSO findings in order to verify the solution. It can be clearly observed from the obtained results that the hybrid BFO-PSO algorithm modifies the solution more precisely and consistently.Research limitations/implicationsThere is no limitation for implementation of proposed methodology in complex systems, and it can, therefore, be used to analyze the behavior of the other repairable systems in higher sensitivity zone.Originality/valueThe performance model of the paint manufacturing system is formulated by utilizing the available uncertain data of the used manufacturing unit. Using these data information, which affects the performance of the system are parameterized in the input failure and repair rate parameters for each sub-system. Further, these parameters are varied to find the sensitivity of a sub-system for system availability among the various sub-systems in order to predict the repair priorities for different sub-systems. The findings of the present study show their correspondence with the system experience and highlight the various availability measures for the system analyst in maintenance planning.

Behavioral analysis and availability optimization of complex repairable industrial system using particle swarm optimization

The complexity in industrial system design under specific practical constraints has a great impact on the range of prediction in system behavior. The data collected in such conditions lead to the high range of uncertainties and the consequence is a possibility of low system performance. Thus, the main objective of the present study is to analyze the system behavior and remove the uncertainties up to the desired accuracy. For this, the mathematical formulation of the system is carried out using probabilistic approach i. e. Markov process. The input failure and repair rate parameters of various sub-systems used in the mathematical expression are considered as constant and statistically independent. Further, the particle swarm optimization (PSO) technique has been used to optimize the system performance in order to improve the system efficiency. A complex repairable system of ton container manufacturing plant has been considered to demonstrate the effectiveness of proposed methodology.

Stochastic modelling and availability analysis of a critical engineering system

Purpose The purpose of this paper is to present the technique for evaluating the performance of a condensate system of a coal-based thermal power plant situated in the northern part of India. The data which used for system availability evaluation are not precise and are uncertain and, further, collected from concerned power plant history sheets and from discussion through plant personnel. Design/methodology/approach In the proposed model, traditional Markov birth-death process using a probabilistic approach is used to analyze the performance of a complex repairable condensate system of power plant up to a desired degree of accuracy. This approach has been demonstrated by breaking the condensate system into six subsystems arranged in series with two feasible states, namely, working and failed, labeled in a transition diagram and modeled as a Markov process, using Chapman–Kolmogorov equations, which are used for development of a probabilistic stochastic model for availability analysis in a more effecting manner, considering some suitable assumptions. Findings This study of analysis of reliability and availability can help in increasing the plant production and performance. The analysis is done with the help of availability matrices, which are developed using different combinations of failures and repair rates of all subsystems. To achieve the goal of maximum power generation, it is required to run the various subsystem of the concerned system of plant, failure free for a long duration. Therefore, the present approach may be a more powerful analysis tool to access the performance of all subsystems of a condensate system in terms of availability level achieved in availability matrices. The results of present study are found to be highly beneficial to the plant management for making maintenance decisions. Originality/value The present paper suggests a suitable technique for stochastic modeling and availability evaluation of an industrial system using Markovian approach and drawing a transition diagram to represent the operational behavior of the system. The present methodology includes the advantage of the ability to model and develop a more complex industrial system and helps in improving the performance and handling the uncertainties and possibilities of an industrial system.

Mission Reliability Simulation Method of Ship Propulsive System

For the problems such as data acquisition and process description ability on the reliability research of multistage complex repairable system in traditional statistical evaluation model based on statistic and interpretation calculation model based on reliability block diagram. An object-oriented hierarchical and modular simulation method was used to decompose the task process into several sub-processes according to the task target. And then through the modeling of internal running rules of sub-processes, a mission reliability simulation model of a complex repairable ship propulsive system with multitask process and multiuse state was established, and the effect of gradual failures were also integrate into the simulation of the task process. Finally, a certain type of marine steam power system was taken as an example to verify the feasibility of this method.

Failure time distributions for complex equipment

AbstractThe exponential distribution is inadequate as a failure time model for most components; however, under certain conditions (in particular, that component failure rates are small and mutually independent, and failed components are immediately replaced or perfectly repaired), it is applicable to complex repairable systems with large numbers of components in series, regardless of component distributions, as shown by Drenick in 1960. This result implies that system behavior may become simpler as more components are added. We review necessary conditions for the result and present some simulation studies to assess how well it holds in systems with finite numbers of components. We also note that Drenick's result is analogous to similar results in other systems disciplines, again resulting in simpler behavior as the number of entities in the system increases.

PRINCIPLES OF CREATION OF AIR DEFENCE WEAPONS AND MILITARY EQUIPMENT OPERATIONS ENGINEERING INTEGRATED SYSTEM

In the course of the reformation of the Armed Forces of the Russian Federation, military maintenance units were significantly reduced or liquidated. It resulted in a shift from the previous WME fleet complex maintenance and repair system to the WME maintenance service system. A main scope of the AD WME maintenance service and repair is performed by industrial enterprises that carry out the assigned works related to the AD WME operations engineering based on the relevant government contracts. Due to this, the works related to the AD WME maintenance service and repair are not coordinated enough. Technical tools and technologies applied by different enterprises when performing the works are not unified. Automated information technologies and maintenance service resource-saving technologies based on them are slowly introduced. There is a necessity to bring together the uncoordinated participants performing the works related to the AD WME fleet operations engineering. For these purposes, a reasoned concept of creation of the integrated system ensuring the AD WME fleet operation is suggested. The integrated system includes in its area of responsibility (AOR) the whole territory of the Russian Federation and is aimed at ensuring increase of the AD WME fleet readiness level by means of work management centralisation, applied technologies typification and unification, development and introduction of innovative solutions and technologies into working practice on the AD WME maintenance service and repair.

Reliability Analysis of Repairable Systems Based on a Two-Segment Bathtub-Shaped Failure Intensity Function

The reliability analysis of complex repairable systems is an important but computationally intensive task that requires fitting the failure data of whole life cycles well. Because the existing reliability models are mainly based on the assumptions that systems are either unrepairable or will go through an “as good as new” type of repair which does not describe actual situations effectively and precisely, a two-segment failure intensity model based on sectional non-homogeneous Poisson process is developed. This model is capable of analyzing repairable systems with bathtub-shaped failure intensity. It considers minimal maintenance activities and preserves the time series of failures based on the whole life cycle. The advantages of this model lie in its flexibility to describe monotonic, non-monotonic failure intensities and its practicality to determine the burn-in or replacement time for repairable systems. Three real lifetime failure data sets are applied to illustrate the developed model. The results show that the model performs well regarding the Akaike information criterion value, mean squared errors, and Cramer–von Mises values.

Nonhomogeneous Poisson process with nonparametric frailty and covariates

The nonhomogeneous Poisson process is commonly used in the modeling of failure times of complex repairable systems. In practice there may be a substantial heterogeneity in the failure behavior among apparently identical repairable systems. In this paper we introduce a new approach for statistical modeling of failures and the corresponding statistical inference when there is both an observable and unobservable heterogeneity between such systems. The approach is partly nonparametric and hence avoids making restrictive assumptions about the underlying process. The main feature of the approach is the elimination of the effect of unobservable heterogeneity, which leaves an optimization problem involving the observable covariates only. The new method is introduced in a power law process setting and can easily be extended to general nonhomogeneous Poisson process. The satisfactory performance of the method is verified in an extensive simulation study as well as in a case study, and the method compares favorably to the gamma frailty model and to the classical regression model not assuming an unobserved heterogeneity. The approach can be adapted for a wide class of models.

An analysis to multi-state manufacturing system with common cause failure and waiting repair strategy

AbstractThis paper presents a mathematical model for a multi-state complex repairable manufacturing system with four types of failures such as common cause, partial, human and catastrophic incorporating waiting time to repair. As regards to repairing of the system, it has to be wait for repair due to unavailability of repair facility after common cause failure. The system may also fail due to catastrophic failure and due to incorrect start of the system, which can occur due to an untrained and inexperienced operator (human failure). The analysis is carried out using the supplementary variable technique and Laplace transformation for evaluating reliability measures such as availability, reliability, mean time to failure, mean time to repair, cost benefit analysis and sensitivity analysis of the system. Some graphical illustrations have also been taken to highlight the practical utility of the model.

Optimisation de la cicatrisation en chirurgie dermatologique et gestions des aléas

Scarring is the response elicited by the skin surface to injury and loss of tissue material. Wound healing takes place through a complex natural repair system consisting of vascular, inflammatory and proliferative phenomena, followed by a remodelling and cell apoptosis phase. This incredible repair system is inevitable, but sometimes unpredictable due to individual differences based on multiple factors. The scar is the objective criterion of a skin surgery, both for the patient and the dermsurgeon. It is therefore crucial to establish with the patient during the preoperative consultation, the size and positioning of the expected scar, taking into account the oncologic, anatomic and surgical constraints. Scars can ideally blend into normal skin, but may also give rise to various abnormalities. We can manage and prevent these abnormalities by mastering initial inflammation, that may induce hyperpigmentation and hypertrophy. Early massage using cortocosteroid topic or anti-inflammatory moisturizers may be effective. Random individual scarring may be minimized by a dynamic personalized accompanying scarring.

Imperfect Preventive Maintenance Policy for Complex Systems Based on Bayesian Networks

The main challenge in maintenance planning lies in the realistic modeling of the maintenance policy. This paper is focused on the maintenance optimization of complex repairable systems using Bayesian networks. A new policy is developed for periodic imperfect preventive maintenance policy with minimal repair at failure; this policy allows us to take into consideration several types of preventive maintenance with different efficiency levels. The Bayesian networks are used for complex system modeling, allowing the evaluation of the model parameters. The Weibull parameters and the maintenance efficiency are evaluated thanks to the proposed methodology using Bayesian inference. The approach developed in this paper is applied on a real system, to determine the optimal maintenance plan for a turbo‐pump in oil industry. Copyright © 2016 John Wiley & Sons, Ltd.

Utility-Based Maintenance Optimization for Complex Water-Distribution Systems Using Bayesian Networks

Water supply systems (WSS), as well as other real-world systems, are characterized by complex configurations. For these systems, it is essential to ensure appropriate utility through optimal maintenance planning. The difficulties in decision-making are much increased by lack of information regarding the operation and failure conditions. When maintenance optimization is considered for systems configured as networks, comprising a large number of components, the main challenge is to model the reliability characteristics, such as availability, taking account of the interactions and dependencies between different components. The aim of this paper is to provide an optimal Preventive Maintenance (PM) plan with a view to maximizing the utility of a complex repairable system using Bayesian Networks (BNs). For each node of the BN, the optimal PM periodicity is obtained, in accordance with the policy of periodic imperfect PM with minimal repair at failure. The system availability is then computed, by Bayesian inference, for various combinations of nodes, or subsystems, periodicities and partial renewals before the complete renewal of the whole system. A utility function is then introduced to provide the maintenance plan for the system, leading to the implementation of the best policy. The methodology is illustrated by numerical application on WSS.

Stochastic Analysis of Complex Repairable System with Deliberate Failure Emphasizing Reboot Delay

Present paper discusses the reliability analysis of a complex system which consists of two repairable subsystems namely A and B connected in series. A is a linear consecutive 2 out of 3:F system and B is a 1 out of n: F system. In this study, two types of failure such as deliberate failure and critical failure have been taken into consideration incorporating reboot delay. Various transition state probabilities, reliability, availability, M.T.T.F., cost analysis, and sensitivity analysis have been obtained along with the steady state behavior of the system. At the end, some special cases of the system have been taken.

Modelling repairable systems with an early life under competing risks and asymmetric virtual age

In this paper, complex repairable systems presenting infant failures are considered. Different maintenance activities can be carried out such as corrective maintenances and planned preventive maintenances. The maintenance process is described using the competing risks framework under imperfect maintenance. Asymmetric virtual age models are assumed to characterize the maintenance efficiency. Statistical inference procedures are developed considering whether or not the causes of failure are recorded. Properties of the model are presented along with numerical estimations and an application to real data.

A multi-constrained maintenance scheduling optimization model for a hydrocarbon processing facility

A maintenance scheduling optimization model considering equipment risk, total maintenance cost, system reliability and availability is proposed. This work is motivated by gas processing operator’s concern of high maintenance cost, poor availability and reliability caused by inefficient maintenance scheduling. The approach presented in this article addresses the optimization of maintenance cost by efficiently scheduling maintenance task subject to reliability and availability constraints. Four maintenance actions are considered for each equipment, namely, corrective, replacement, maintenance and inspection. The proposed solution develops maintenance schedules for complex repairable system with equipment operating in series. Two single-objective, nonlinear mathematical models are presented to find the optimal maintenance cost subject to reliability and system reliability subject to availability constraint. A goal programming model is also proposed to simultaneously deal with multiple criteria based on their importance and defined goals. A gas absorption system of a hydrocarbon processing facility is used to ensure the practicality of the proposed formulation to real industry problems. A comparison of existing and proposed formulation is carried out to show that the proposed optimization approach is an efficient method for optimizing maintenance schedules and flexibility to adjust schedules in a complex operating system.

Evaluating the Impact of Spare Parts Pooling Strategy on the Maintenance of Unreliable Repairable Systems

In today's industries, optimal spare parts provisioning plays a critical role towards sustaining the asset's operational capabilities. In this context, spare parts pooling is increasingly mentioned as a plausible approach for optimizing spare parts management, more so for repairable systems. In this sense, several frameworks are proposed in literature, though largely analytical, thus limiting their capabilities with respect to modeling complex repairable systems. Moreover, the nature of interactions often account for aspects such as system reliability, imperfect component reconditioning, repair capacity and spare parts inventory that is multi-echelon in nature. To realistically model such complexities, simulation approaches are often explored. This paper presents a discrete event simulation modeling study. The simulation model mimics the impact of several aspects on unreliable repairable systems. The aspects include the repair capacity, component reconditioning process, and multi-echelon spare part provisioning strategy. Moreover, the influence of spare parts quality aspects is evaluated and demonstrated in the case study of critical system for a thermal power plant.

Performance and cost analysis of repairable systems under imperfect repair

Operating systems do not consistently perform their intended function or mission due to improper maintenance and degradation, and thus eventually fail. Upon failure, they are either repaired or replaced; critical and non–expensive systems are usually replaced, whereas expensive and non–critical systems are repaired. Repair can be perfect, where the failed system becomes as good as new, or imperfect. In imperfect repair, a failed system is repaired several times before complete replacement. In this paper, the behaviour of complex repairable systems undergoing imperfect repair is examined. In this regard, an optimisation model is developed with the objective of minimising system's cost at a desired level of availability. Analytical expressions for availability and the optimal cost are derived for simple and non–complex systems; however, simulation is used for assessing complex systems. The analysis revealed that optimal solutions are highly dependent on the structure of the configuration and the desired level of performance.