Standby Components Research Articles

Sensitivity Analysis for a Redundant System with Random Inspection and R Repairmen Under Repair Pressure

This study examines a repairable K -out-of- N + W : G system with warm standby components and R repairmen, where common cause failure (CCF), random inspection, and a repair pressure coefficient are involved. Due to the typical non-self-announcement of the standby component’s failure, performing a random inspection on warm standby components is necessary to find whether there are failed standby components. When the number of failed components is larger than R (heavy loading occurs), all the repairmen increase their repair rates under a repair pressure coefficient to reduce the waiting queue and enhance the reliability and availability of the system. By adopting a matrix-analytic method and Markov renewal theory, this study provides the steady-state distribution and some reliability indices mean time to first failure time (MTTFF) and reliability function from the arbitrary initial state. By some numerical experiments, sensitivity analysis is finally given to show the effect of the system parameters on some performance measures.

A new dynamic model to optimize the reliability of the series-parallel systems under warm standby components

<p style='text-indent:20px;'>Redundancy allocation problem (RAP) is a common technique for increasing the reliability of systems. In this paper, a new model for the RAP is introduced that takes into account the warm standby and mixed strategy, the model dynamics, and the type of the strategy in redundancy allocation problems. A recursive formula is first obtained for the reliability function in the dynamic warm standby and mixed redundancy strategies that leverages the success mode analysis and works for any arbitrary failure-time distribution. Failure rates for warm standby units change before and after their replacement with a damaged unit, and, therefore, the reliability function in warm standby varies with time (i.e., the model is dynamic). Although dynamic models are commonplace in practice, they are more challenging to assess than static models, which have been mainly considered in the literature. An optimization problem is then formulated to select the best redundancy strategy and redundancy levels. Genetic algorithm and particle swarm optimization are leveraged to solve the problem. Finally, the efficiency of the presented method is verified through a numerical example. The experimental results verify that the proposed model for RAP significantly improves the system reliability, which can be of vital importance for system designers.</p>

Availability Optimization Decision Support Design System for Different Repairable n-Stage Mixed Systems

This study attempts to propose an availability optimization decision support design system for repairable n-stage mixed systems, in which different combinations of subsystems, such as parallel, standby, and k-out-of-q, are connected in series configuration. The enumeration method, tabu search, simulated annealing, non-equilibrium simulated annealing, and the modified redundancy allocation heuristic combined with a modified genetic algorithm will be proposed to solve the system availability optimization problem and further determine the appropriate system configuration design. Several simulated cases are conducted by following the procedural flow of the proposed availability optimization decision support design system to reach the optimal allocations of the component redundancy amount, the optimal repair rates, and the optimal failure rates of all subsystems to minimize the total system cost under several configuration constraints for different repairable n-stage mixed systems. Simulated results display that the proposed availability optimization decision support design system can definitely take advantage of different component redundancy system designs, including the parallel-series system, n-stage standby system, n-stage k-out-of-q system, and n-stage mixed system, to save a lot of cost and meet the high level of the system availability requirement compared to the n-stage single component series system. Additionally, the results for all proposed combined methods also show that the parallel-series system can obviously reach the same level of system availability requirement with less system total cost, in contrast to the n-stage standby system, by presuming the identical deteriorating probability for both the operating components and the standby components. The performance comparisons of five proposed combined methods for four proposed system configurations are analyzed comprehensively. It can be concluded that the performances of the modified redundancy allocation heuristic method, combined with a modified genetic algorithm on the criteria of the optimal system costs for four proposed system configurations, are not only superior to the other four combined methods, but also to the performances on the criteria of CPU running time for four proposed system configurations.

Influence of the three different types of standby components on the performance of a k–out–of–n:F system in the dynamic stress–strength model

We consider the effect of adding three different types of standby components to a k–out–of–n:F system based on the dynamic stress–strength model. For this purpose, the system reliability in the three different types of standby components, i.e., cold, warm and hot standby components, are calculated. Also, it is assumed that the stress and the strength components follow the Weibull distribution and the Weibull process, respectively. The maximum likelihood estimators of the redundancy system reliability are obtained. In addition, a simulation study is performed based on the Monte Carlo method. Finally, the results are illustrated via a practical example that is the time of successive failures of the air conditioning system of every member of a fleet of jet airplanes.

Optimal standby activation in m‐out‐of‐n systems: A preventive approach

AbstractThis article investigates an optimal preventive standby activation policy for an ‐out‐of‐ redundant system. We assume that for such a system, which starts operating at time , a standby component will be activated at either the failure time of the system or at a predetermined time , whichever occurs first. We first obtain the system reliability function under this switching policy as a function of . Then, we investigate the optimal switching time so that the mean time to failure of the system is maximized. The results indicate that the existence of an optimal value of depends on the lifetime of the standby redundancy and its virtual age in the standby state. Some illustrative examples are presented to examine the theoretical outcomes.

A hybrid Markov model–PSO algorithm for the exact solution of reliability optimization problems with cold standby components

A hybrid Markov model–PSO algorithm for the exact solution of reliability optimization problems with cold standby components

Reliability analysis of consecutive-k-out-of-r-from-n subsystems: F balanced systems with load sharing

In this paper, a consecutive-k-out-of-r-from-n subsystems: F balanced system with load sharing is proposed, which takes both linear and circular structures into consideration. Such a system has n subsystems and each subsystem consists of several sectors that have multiple components. Each subsystem should keep balance when it works and the concept of balance means that the number of working components should be the same in any sector belonging to the same subsystem. The system maintains balance by forcing down working components to standby or resuming standby components to operate. The failure or standby of a component will result in a higher load on each remaining working component, thereby inducing a higher hazard rate for them. The whole system fails if it contains a set of r consecutive subsystems in which at least k subsystems break down. The Markov process method and finite Markov chain imbedding approach are applied to analyze the system reliability. Numerical examples are presented to show the effectiveness of the studied model and proposed method.

Joint optimization of mission aborts and allocation of standby components considering mission loss

Mission abort is an effective way to enhance system safety during mission execution. Existing multi-attempt mission abort models can be divided into two main categories according to the additivity of completed missions: non-accumulative models and completely cumulative models. This paper studies the optimal mission abort and allocation of standby components policies for the k-out-of-(n+m):F system considering partial mission loss. During each attempt, the mission abort decision is dynamically controlled via predetermined abort thresholds and the rescue procedure (RP) starts immediately upon mission abort. In most studies, after a successful RP, the system is commonly restored to an ‘as good as new’ state with the underlying assumption that the standby components are always adequate. However, due to factors such as cost and capacity, the number of standby components may be limited. This paper proposes a dynamic allocation policy of a fixed number of standby components. The aim is to determine the optimal number of the failed components be replaced after each RP. By using a recursive algorithm, mission reliability and system survivability are derived. The objective is to minimize the expected cost and balance the mission reliability and the system survivability. The advantage of the proposed policy is justified by the policy comparison. Finally, the obtained results are demonstrated considering an autonomous underwater vehicle performing a photography mission.

Reliability optimization problem with the mixed strategy, degrading components, and a periodic inspection and maintenance policy

Redundant components stored in the standby mode degrade over time due to harsh environmental conditions. A proper inspection and maintenance plan is, therefore, required to combat performance degradation while maintaining system reliability. The present study presents a new reliability model for a system composed of simultaneously active and standby components with the standby ones prone to degradation. In this model, a periodic inspection and preventive maintenance policy is adopted for the standby components that needs to be duly scheduled. Thus, not only should the optimal component types be identified but the number of active and standby components in each subsystem needs to be determined as well. The objective function is to find optimal inspection intervals that ensure maximum system reliability with system constraints duly considered. For this purpose, a model is proposed for the problem at hand and a genetic algorithm is developed for its solution. Finally, a numerical example and an industrial case study are presented to verify the efficiency of the proposed model through sensitivity analysis in the presence and absence of the inspection policy.

Research and Application of System Reliability Allocation based on Markov Chain

In order to maximize the reliability of a weapon system, a continuous time Markov method is used to allocate the system reliability redundancy. The choice of reliability redundancy strategy is considered as a decision variable, which strives to maximize the reliability of the system under the constraints of cost, weight and volume. In many model applications studied in the past, each system generally adopts a single active redundancy strategy or cold standby redundancy strategy. In the model cited in this paper, each subsystem can adopt the selective redundancy strategy of active or cold standby components, increasing the flexibility of the redundancy strategy, and establishing the precise reliability function for the system using the selective redundancy strategy through the continuous time Markov chain. Because reliability redundancy assignment is a nonlinear mixed integer programming problem, a pseudo-parallel genetic algorithm is designed to solve the model. Finally, the results show that the reliability of the weapon system is significantly improved.

Statistical inference of the stress-strength reliability and mean remaining strength of series system with cold standby redundancy at system and component levels

In this study, we consider the stress-strength reliability and mean remaining strength of a series system with cold standby redundancy at the component and system levels. Classical and Bayesian approaches are studied in order to obtain the estimates when the underlying stress, strength and standby components follow the exponential distribution with different parameters. Bayes estimates are approximated by using Lindley’s approximation and Markov Chain Monte Carlo methods. Asymptotic confidence intervals and highest probability density credible intervals are constructed. We perform Monte Carlo simulations to compare the performance of proposed estimates. A real data set is analyzed for the purpose of illustration.

Optimal preventive switching of components in degrading systems

In practice, at many instances, it is important to maintain the failure-free performance of components in a standby system, as each sudden failure of an operating component can result in a failure of a system, e.g., due to imperfect or/and ‘non-instantaneous’ switching on failure and related adverse effects. Therefore, the scheduled preventive switching/replacement to the standby component that can be executed without these consequences is one of the effective methods for increasing reliability characteristics of such systems, especially in the safety-critical applications. In this paper, the corresponding optimal strategy for switching is described and justified for the cold standby system of two aging components with degradation modeled by the counting Poisson and gamma processes. An inspection is carried out at some optimally predetermined time and based on the observed degradation switching is performed after the optimally obtained delay. Detailed numerical examples illustrate our findings.

A probabilistic model to evaluate the resilience of unattended machinery plants in autonomous ships

Over the next few years, digitalization and automation are expected to be key drivers for maritime transport innovation to be key drivers for maritime transportation innovation. This revolutionary shift in the shipping industry will heavily impact the reliability of the machinery which is intended to be operated remotely with minimum support from humans. Despite a large amount of research into autonomous navigation and control systems in maritime transportation, the evaluation of unattended engine rooms has received very little attention. For autonomous vessels to be effective during their unmanned mission, it is essential for the engine room understand its health condition and self-manage performance. The unattended machinery plant (UMP) should be resilient enough to have the ability to survive and recover from unexpected perturbations, disruptions, and operational degradations. Otherwise, the system may require unplanned maintenance or the operation will stop. Therefore, the UMP must continue its operation without human intervention and safely return the ship to port. This paper aims to develop a machine learning-based model to predict an UMP's performance and estimate how long the engine room can operate without human assistance. A Random Process Tree is used to model failures in the unattended components, while a Hierarchical Bayesian Inference is adopted to facilitate the prediction of unknown parameters in the process. A probabilistic Bayesian Network developed and evaluated the dependent relationship between active and standby components to assess the effect of redundant units in the performance of unattended machinery. The present framework will provide helpful additional information to evaluate the associate uncertainties and predict the untoward events that put the engine room at risk. The results highlight the model's ability to predict the UMP's trusted operation period and evaluate an unattended engine room's resilience. A real case study of a merchant vessel used for short sea shipping in European waters is considered to demonstrate the model's application.

Prescheduled switching time: A new strategy for systems with standby components

Reliability improvement is a major industrial concern. A commonly known approach to improve the reliability of a system is the so-called ‘redundancy strategy’ in which redundant components are used in the subsystems to achieve considerable enhancements in total system reliability. Generally, the two active and standby strategies are the ones most used in addressing reliability optimization problems (ROPs). The present article proposes a new structure for the standby strategy that revolutionizes its conventional structure into one that considers a predefined time at which a component starts operation without the need for a switching system to activate it. The standby component actively enters the system at the predefined time; this naturally improves system reliability by relaxing the need for the switch to a considerable extent, which is especially useful in cases of a poor switching system. The objective in this problem is to determine the best time for the component to join the system. According to the new strategy, the system typically uses its switching system whenever there is a need for a component to join the subsystem but the component may also join the system automatically at a predefined time in order to reduce the likelihood of system failure.

Reliability evaluation of demand-based warm standby systems with capacity storage

Warm standby is an energy-saving redundancy technique that consumes less energy than a conventional hot standby method. It can be naturally integrated with an energy storage technique to enhance system reliability. However, the integration approach of both techniques and the advantage it affords to system reliability have not been reported in literature. This study resolves this limitation by formulating a novel reliability model for demand-based warm standby systems with capacity storage. In this model, the chronological characteristics of warm standby components are explicitly explored before and after their activation. Moreover, different utilization sequences of warm standby components and storage components are embedded to analyze the effects of these sequences on system reliability. A multi-valued decision diagram is developed to evaluate system reliability considering successful activation probabilities of warm standby components. This diagram is applicable to arbitrary component lifetime distributions and warm standby systems. Numerical examples are presented to verify the application of the proposed methodology.

Analysis of the two-unit cold standby repairable system with damage and repair time dependency via matrix-exponential distributions

ABSTRACT In this paper, two-unit standby repairable system is studied via matrix-exponential distributions. The system under concern consists of one active and one standby components, and fails if either a damage size upon the failure of the active component is larger than a repair limit or the repair time of the failed unit exceeds the lifetime of the active unit, whichever happens first. Under the assumption that the damage size and repair time are statistically dependent, the Laplace transform of the system’s lifetime is obtained. The Laplace transform is shown to be rational under particular cases, and the reliability evaluation of the system is performed via well-known distributional properties of the matrix-exponential distributions. The problem of estimating the unknown parameters of the operation time and repair time distributions is also discussed based on system’s lifetime data.

Joint optimization of mission abort and component switching policies for multistate warm standby systems

Safety-critical systems have been widely applied in various practical engineering fields to perform specific missions. Failures of such systems will lead to significant damage, thus, mission abort polices can be implemented to enhance system survivability. Warm standby is one of the most commonly used techniques in mission-critical and safety-critical systems to reduce failure risk during mission execution. Switching between working component and standby component is a feasible way to balance component degradation and extend system lifetime. Motivated by these results, we propose a joint optimization model that captures both component switching and mission abort policies for multistate warm standby systems. The optimal component switching and mission abort policies based on component condition are obtained to balance the trade-off between mission success probability and system survivability and to minimize the long-run expected total economic loss. A numerical study of virtual machine system is conducted to demonstrate the proposed model and obtained results.

Reliability analysis of heat exchanging system of deep-sea manned submersibles using Markov model

This study introduces reliability assessment models for heat exchanging systems in deep-sea manned submersibles based on the Markov analysis method. Three distinct design systems—a series solution and two series–parallel solutions, considering cold and hot standby components—were presented. In addition, the state transition models of the Markov random process were established with relation to common-cause failures. Moreover, the calculation methods were derived for the two reliability indices of the heat exchange system: steady state availability and mean time to failure. Thus, the reliability of various design solution cases was investigated by comparing the reliability indices with variations in the failure and repair rates. The influence of common-cause factors on the system reliability was discussed as well. Furthermore, design solutions were suggested based on the results of the quantitative calculations and analysis.

Reliability analysis and state transfer scheduling optimization of degrading load-sharing system equipped with warm standby components

Standby redundancy can meet system safety requirements in industries with high reliability standards. To evaluate reliability of standby systems, failure dependency among components has to be considered especially when systems have load-sharing characteristics. In this paper, a reliability analysis and state transfer scheduling optimization framework is proposed for the load-sharing 1-out-of- N: G system equipped with M warm standby components and subject to continuous degradation process. First, the system reliability function considering multiple dependent components is derived in a recursive way. Then, a Monte Carlo method is developed and the closed Newton-Cotes quadrature rule is invoked for the system reliability quantification. Besides, likelihood functions are constructed based on the measurement information to estimate the model parameters of both active and standby components, whose degradation paths are modeled by the step-wise drifted Wiener processes. Finally, the system state transfer scheduling is optimized by the genetic algorithm to maximize the system reliability at mission time. The proposed methodology and its effectiveness are illustrated through a case study referring to a simplified aircraft hydraulic system.

Reliability Optimization of a k-out-of-n Series-Parallel System with Warm Standby Components

Reliability Optimization of a k-out-of-n Series-Parallel System with Warm Standby Components