Standby System Research Articles

Reliability inference of stress-strength for a K-out-of-N: G mixed standby system with switching failure

In this paper, the statistical inference of the stress-strength reliability for a K-out-of-N: G mixed standby system with switching failure is investigated. It is assumed that the strength of the component itself and the external stress to which it is subjected are random variables that obey the same distribution. If an operating component fails, the warm standby component will be immediately switched to the operating one. During this switchover, the warm standby component is subject to switching failure with a certain probability. Based on Weibull distribution and generalized half-logistic distribution, the reliability indices of stress-strength model under two different distributions are derived, respectively. For the two types of distribution of stress and strength, the maximum likelihood estimator, maximum spacing estimator, Bayesian estimator, asymptotic confidence interval and bootstrap-p confidence interval of the reliability are obtained, respectively. The Monte Carlo method is applied in the numerical simulation to realize the simulation inference of the system reliability. The performance of the above inference methods is analyzed and compared. The influence of parameters of stress and strength on the system reliability is analyzed graphically.

Standby and inspection policy optimization in systems exposed to common and operational shock processes

Motivated by practical applications like data storage, product defect detection, medical imagining, and sensing, this paper puts forward a new inspected standby system model where only one element can be online operating and only one element can stay in the standby mode at any time. Both operating and standby elements are exposed to random common shocks, causing their deterioration and even failures. The operating element may also be deteriorated by random operational shocks. The system undergoes periodic inspections to determine the refill of the operating or standby element. A new optimization problem is formulated and solved to determine the inspection and standby element addition policy with the objective to minimize the expected mission cost (EMC) attributed to factors including system downtime, number of inspections, element modes and failures, element activation and mode transitions. A new and efficient system state transition-based numerical algorithm is proposed to evaluate the EMC. A case study of a standby sensor system is provided to demonstrate the proposed model and impacts of several cost parameters as well as shock rates on the EMC and the optimal inspection and standby element addition policy, leading to insightful managerial guidelines for the system design and operation.

Standby System Reliability Modeling with Cost and Sensitivity Analysis

Maintaining a reliable water treatment plant is paramount for a consistent and dependable supply of safe drinking water to communities. This study employs an integrated approach, leveraging advanced methodologies including Markov processes, supplementary variable technique, and Laplace transformation. The research seeks to comprehensively model the plant’s dynamic behavior, transitions between operational states, and the influence of maintenance practices. By employing these methodologies, the research aims to enhance the precision of critical reliability metrics such as reliability and availability. The study further incorporates thorough cost analysis, shedding light on the economic implications of reliability-enhancing measures. Additionally, sensitivity analysis is employed to identify critical variables influencing plant performance and economic efficiency. The insights garnered from this study are expected to provide a sophisticated understanding of the water treatment plant’s performance, fostering effective decision-making strategies to optimize maintenance protocols and ensure the uninterrupted delivery of clean and secure water to communities.

Condition-based switching, loading, and age-based maintenance policies for standby systems

Standby techniques are widely incorporated in structural design to enhance the inherent reliability of systems. To further leverage the system performance during operation, decision-makers can adopt operational policies to manage system degradation. Specifically, at the system level, unit switching that dynamically determines the online unit contributes to avoiding unexpected shutdowns. At the unit level, adjusting load levels to manage the trade-off between condition degradation and revenue accumulation is crucial for maximizing profit. Additionally, adopting age-based maintenance as a tactical decision, which effectively facilitates the integration of maintenance resources, can be implemented to restore a degraded system. For instance, maintenance is typically scheduled at fixed moments for multi-generator power systems located in remote areas. In between maintenance moments, the proactive switching of generators can ensure uninterrupted output, and the adjustment of load levels for online generators helps to maximize output. Motivated by such engineering practices, this paper investigates condition-based switching, loading, and age-based maintenance policies for standby systems to maximize the expected profit rate in the long-run horizon. The problem is formulated as a Markov decision process. The structural properties of the control-limit switching and monotone loading policies are analyzed for easy policy implementation and efficient problem solutions. For comparative purposes, several heuristic policies are proposed and evaluated. Finally, numerical examples are presented to validate theoretical results and illustrate the superiority of the proposed risk control policy.

Profit analysis of a two-unit similar cold standby system with endurance time and preventive maintenance

Profit analysis of a two-unit similar cold standby system with endurance time and preventive maintenance

A two Identical Unit Cold Standby System with two Repairmen and Correlated Failure and Repair Times

The paper deals with the stochastic analysis of a two identical unit cold standby system model. As soon as a unit fails it is attended first by assistant repairman, who completes its repair to make it as good as new with fixed known probability p. Otherwise, with probability q( =1-p), the unit repaired by assistant repairman goes to expert repairman so as to make it as good as new. Both the repairmen are always available with the system and they work their jobs simultaneously. The failure time and repair time by assistant repairman are correlated random variables having their joint distribution as Bivariate exponential. Various measures of system effectiveness useful to system designers and operation managers are obtained by using regenerative point technique. The MTSF and steady state profit have also been studied through graphs in respect of parameter alpha for three different values of repair rate by expert repairman and two different values of correlation coefficient between failure and repair times and important conclusions are drawn.. KEYWORDS :Bivariate exponential distribution, Mean time to system failure (MTSF), Regenerative point, Laplace transform.

Lifetime prediction and replacement optimization for a standby system considering storage failures of spare parts

Spare parts redundancy is an effective way to improve system reliability and prolong system lifetime. Generally, preventive maintenance and inventory management of a standby system can significantly reduce operating and maintenance costs, particularly crucial in industrial systems. Additionally, during storage, spare parts may experience degradation failure due to internal mechanisms and sudden failure due to external shocks, complicating the health management of the standby system. Unfortunately, few existing studies consider the impact of spare parts’ competing failures in standby systems. To address this gap, this paper proposes a novel method for predicting the lifetime and optimizing the maintenance policy of nonlinear degradation standby systems, considering both degradation and sudden failures of spare parts. Firstly, we established the degradation model under the competing failure of spare parts and derived the analytical formula for life prediction of the standby system in the presence of competing failures, assuming that sudden failure follows the Weibull distribution. Furthermore, we developed a joint optimization model for replacement maintenance and inventory management based on predictive information, aiming to minimize the expected cost, in which the block replacement interval and the number of spare parts are treated as decision variables. Finally, the effectiveness and potential application value of the proposed method are verified through numerical simulation and a case study.

Reliability and Residual Life of Cold Standby Systems

In this study, we conduct a reliability characterisation study of cold standby systems. Utilising synthetic rectangular formulas and cold preparedness equivalent models for cold standby systems, we analyse the lifetimes of several typical configurations, including series, parallel, and k/n:m voting systems. This study proposes system equivalent models for various types of cold standby systems, all composed of components that follow the same exponential distribution. We use the equivalent model to determine the optimal timing for the use of cold spares and derive the reliability function and residual lifetime function for each type of system. To demonstrate the validity of our model, the Monte Carlo simulation is strategically designed based on the system failure rate function. The experimental results are then compared with those obtained from the numerical model, highlighting that the numerical method incurs a lower time cost.

Availability Evaluation of Warm Standby System with Fault Detection Delay and General Repair Times

This study analyzed the availability of a warm standby system that works with fault detection delay and general repair times. The time-to-detection delay is also considered as exponentially distributed. The detection state is used to detect the faults in the failed unit. The steady state availability of the system is obtained by using supplementary variable technique. Three types of repair time distributions are compared to find the best one.

Optimal maintenance policy considering imperfect switching for a multi‐state warm standby system

AbstractA novel maintenance policy for a two‐component warm standby system with multiple standby states is presented in this paper. Two standby states, that is, cold and warm standby, for components in the system are considered. Components can realize the transition from the cold standby to the warm standby state by periodic switching, intended to shorten recovery time and save system operating costs. Preventive maintenance (PM) and preventive switching (PS) of components are considered. In the PS strategy, the standby component can switch before the operating component fails. In addition, additional standby failure modes based on idle time are studied. Derive the long‐term average cost of the system through the semi‐regenerative process. A numerical example eventually verifies the feasibility of this paper's proposed maintenance and switching strategy.

Role of individual component failure in the performance of a 1-out-of-3 cold standby system: A Markov model approach

Abstract The current work assesses the availability and reliability of repairable 1-out-of-3 cold standby systems, undergoing switching failure, by the Markov models, where a finite set of differential equations govern the repairable system. A numerical approach based on KAOS programming in MATLAB is employed to solve the differential equations. Thus, numerical solutions for the time-dependent availability can then be obtained. The Laplace transform method is used for the reliability model to find the explicit form of the reliability function and the mean time to failure (MTTF) of the system. An availability estimation model for a 1-out-of-3 cold standby system is developed and the main electrical power network, local electricity generator, and domestic electricity generator are studied. Finally, the model is analyzed to illustrate the effect of both failures and repair rates as well as component capacity partitioning on system availability and MTTF.

Time-domain computation of the reliability of standby systems with and without priority under general repair

In this paper, we consider a two-unit, non-identical cold standby system under general repair with or without priority. In the prioritized case, one unit in the system is designated to serve as standby only, that is, the standby unit is only utilized when the main unit has failed and is being repaired, whereas in the non-prioritized case, both units serve alternately as standby with each unit being put to standby mode when its repair is complete. The effect of each repair is modelled by a degree θ, 0≤θ≤1, where θ=0 means full repair (or full replacement) and θ=1 means minimal repair. For these cases, and for arbitrary distributions of the lifetimes and repair times of the units in the system, we obtain the reliability function in the time domain as opposed to the Laplace domain. Our functions utilize results from classical and generalized renewal theory results. We validated our formulas on various in-house standby configurations, as well as standby configurations occurring in coolant systems of nuclear power plants.

1-out-of-n standby systems with storages and activation moment-dependent component operation time limit

This paper pioneers the modeling and optimization of a 1-out-of-n standby system with a required mission time and non-identical components, whose operation times are limited and dependent on their activation time and dynamic resource supply and storage units. Each component contains a main operating unit characterized by certain time-to-failure distribution and mode-dependent resource demands, a resource generator characterized by a time-varying resource generation rate, and a storage unit characterized by an initial amount of resource and the maximum capacity. The resource generator and storage interact collaboratively to supply the dynamic resource demand of the main unit. Based on an event transition probabilistic model, a new numerical algorithm is proposed for assessing the mission success probability (MSP) of the considered standby system. An optimization problem is further formulated and solved, which determines the activation sequence of standby components maximizing the MSP. A detailed case study of a standby sensor system with the photo-voltaic power supply source and battery storage is provided to demonstrate the proposed model and examine the effects of several system parameters on the MSP and optimization solutions. Several useful findings related to the MSP improvement are revealed from the case study.

Allocation and activation of resource constrained shock-exposed components in heterogeneous 1-out-of-n standby system

Allocation and activation of resource constrained shock-exposed components in heterogeneous 1-out-of-n standby system

A Condition-Based Maintenance Policy Considering Batch Sizes for Warm Standby Systems With Priority to Repair

A Condition-Based Maintenance Policy Considering Batch Sizes for Warm Standby Systems With Priority to Repair

A subset simulation analysis framework for rapid reliability evaluation of series-parallel cold standby systems

A subset simulation analysis framework for rapid reliability evaluation of series-parallel cold standby systems

Efficient reliability approximation for large k-out-of-n cold standby systems with position-dependent component lifetime distributions

Efficient reliability approximation for large k-out-of-n cold standby systems with position-dependent component lifetime distributions

Reliability and Profitability Analysis of a Mixed Redundancy Standby System comprising Operative Unit along with Hot and Cold Standby

Reliability and Profitability Analysis of a Mixed Redundancy Standby System comprising Operative Unit along with Hot and Cold Standby

Prediction of ash deposition in a biomass boiler using thermomechanical analysis

Retrofitting biomass boilers is a promising strategy for use in generating electricity while reducing greenhouse gas emissions. However, biomass ash deposition on the wall evaporators, platens, and superheaters during operation requires further investigation to ensure their safeties. Herein, a thermomechanical analysis (TMA) method is proposed, and a simulation is conducted using computational fluid dynamics to predict the biomass deposition rates in a biomass boiler. A chemical analysis of biomass ash and a curve fitting of TMA shrinkage are performed and a drop-tube furnace is used to validate the models. Four case studies are based on burner standby systems, and high deposition rates are observed on the wall furnace and hopper and platen superheater. The simulation results are consistent with the experimental results, and the wall furnace and platen superheater exhibited the highest levels of deposition of 1.4–1.6 kg/(m2·h). This method provides a foundation for predicting the locations and rates of deposition in boilers.

A novel condition‐based maintenance policy considering imperfect switching for warm standby systems

AbstractThis paper proposes a novel condition‐based maintenance model for a two‐component warm standby system considering an imperfect switching policy. Unlike the conventional models, joint optimization of preventive and failure switching strategies is investigated. The standby component can be switched preventively when the component reaches the preventive maintenance (PM) threshold before the online one fails. The failure switching is triggered when the online component reaches the corrective maintenance (CM) threshold. In addition, the switching mechanism between the two components is related to the order fulfillment rate. The optimal PM threshold and order fulfillment rate can be derived by minimizing the long‐term average cost rate. A numerical example is provided to demonstrate the robustness of the decision model under different scenarios through sensitivity analysis. Ultimately, the effectiveness of the proposed model in this article was verified by comparing the models.