scholarly journals Bayesian Prediction of the Overhaul Effect on a Repairable System with Bounded Failure Intensity

2011 ◽  
Vol 2011 ◽  
pp. 1-8
Author(s):  
Preeti Wanti Srivastava ◽  
Nidhi Jain
Keyword(s):  
Failure Process ◽  
Repair Process ◽  
Time Interval ◽  
Repairable Systems ◽  
Maintenance Policy ◽  
Numerical Application ◽  
System A ◽  
The Future ◽  
Failure Intensity ◽  
Preventive Maintenance Policy

This paper deals with the Bayes prediction of the future failures of a deteriorating repairable mechanical system subject to minimal repairs and periodic overhauls. To model the effect of overhauls on the reliability of the system a proportional age reduction model is assumed and the 2-parameter Engelhardt-Bain process (2-EBP) is used to model the failure process between two successive overhauls. 2-EBP has an advantage over Power Law Process (PLP) models. It is found that the failure intensity of deteriorating repairable systems attains a finite bound when repeated minimal repair actions are combined with some overhauls. If such a data is analyzed through models with unbounded increasing failure intensity, such as the PLP, then pessimistic estimates of the system reliability will arise and incorrect preventive maintenance policy may be defined. On the basis of the observed data and of a number of suitable prior densities reflecting varied degrees of belief on the failure/repair process and effectiveness of overhauls, the prediction of the future failure times and the number of failures in a future time interval is found. Finally, a numerical application is used to illustrate the advantages from overhauls and sensitivity analysis of the improvement parameter carried out.

Optimal sequential preventive maintenance policy for a repairable system with maintenance windows

2019 ◽  
Vol 234 (4) ◽  
pp. 963-977
Author(s):  
Wenke Gao
Keyword(s):  
Preventive Maintenance ◽  
Existence And Uniqueness ◽  
Optimal Solution ◽  
Intensity Function ◽  
Sensitivity Analyses ◽  
Repairable System ◽  
Maintenance Policy ◽  
Catastrophic Failures ◽  
Failure Intensity ◽  
Preventive Maintenance Policy

Optimization of sequential preventive maintenance has been widely studied. However, some issues need further discussion when considering certain real factors. Hence, an sequential preventive maintenance policy for a mono-unit repairable system with maintenance windows is proposed. The load strength and catastrophic failures of a system are considered in the reliability model, and an optimization of the SPM policy is rigorously proven by an analytical method. The existence and uniqueness of the optimal solution with a monotone increasing failure intensity function are presented in detail. The proof of theory indicates that the length of the maintenance window has an upper bound. Finally, a numerical example and some sensitivity analyses are given to illustrate the proposed sequential preventive maintenance policy. Results show that waste may occur when performing the general sequential preventive maintenance policy and may increase along with the length of the maintenance windows.

Asymptotic failure rate of a Markov deteriorating system with preventive maintenance

2003 ◽  
Vol 40 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Sophie Mercier ◽  
Michel Roussignol
Keyword(s):  
Failure Rate ◽  
Preventive Maintenance ◽  
Sufficient Conditions ◽  
Maintenance Policy ◽  
System A ◽  
Up States ◽  
Finite State ◽  
Degradation Degree ◽  
Preventive Maintenance Policy ◽  
Finite State Space

We consider a system with a finite state space subject to continuous-time Markovian deterioration while running that leads to failure. Failures are instantaneously detected. This system is submitted to sequential checking and preventive maintenance: up states are divided into ‘good’ and ‘degraded’ ones and the system is sequentially checked through perfect and instantaneous inspections until it is found in a degraded up state and stopped to allow maintenance (or until it fails). Time between inspections is random and is chosen at each inspection according to the current degradation degree of the system. Markov renewal equations fulfilled by the reliability of the maintained system are given and an exponential equivalent is derived for the reliability. We prove the existence of an asymptotic failure rate for the maintained system, which we are able to compute. Sufficient conditions are given for the preventive maintenance policy to improve the reliability and the asymptotic failure rate of the system. A numerical example illustrates our study.

scholarly journals DYNAMIC ANALYSIS OF A MULTIVARIATE REWARD PROCESS DEFINED ON THE UMCP WITH APPLICATION TO OPTIMAL PREVENTIVE MAINTENANCE POLICY PROBLEMS IN MANUFACTURING

2013 ◽  
Vol 27 (2) ◽  
pp. 187-208
Author(s):  
Jia-Ping Huang ◽  
Ushio Sumita
Keyword(s):  
Preventive Maintenance ◽  
Manufacturing System ◽  
Counting Process ◽  
Time Interval ◽  
Counting Processes ◽  
Maintenance Policy ◽  
Reward Process ◽  
Probabilistic Flow ◽  
Policy Problems ◽  
Preventive Maintenance Policy

The unified multivariate counting process (UMCP), previously studied by the same authors, enables one to describe most of the existing counting processes in terms of its components, thereby providing a comprehensive view for such processes often defined separately and differently. The purpose of this paper is to study a multivariate reward process defined on the UMCP. By examining the probabilistic flow in its state space, various transform results are obtained. The asymptotic behavior, as t→∞, of the expected univariate reward process in a form of a product of components of the multivariate reward process is studied. As an application, a manufacturing system is considered, where the cumulative profit given a preventive maintenance policy is described as a univariate reward process defined on the UMCP. The optimal preventive maintenance policy is derived numerically by maximizing the cumulative profit over the time interval [0, T].

Progressive maintenance policy for multiple repairable systems with imperfect maintenance

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Garima Sharma ◽  
Rajiv Nandan Rai
Keyword(s):  
Power Plants ◽  
Maintenance Cost ◽  
Mean Residual Life ◽  
Time Interval ◽  
Imperfect Maintenance ◽  
Repairable Systems ◽  
Maintenance Policy ◽  
Data Set ◽  
Content Type ◽  
Maintenance Policies

PurposeDegradation of repairable components may not be similar after each maintenance activity; thus, the classic (traditional-time based) maintenance policies, which consider preventive maintenance (PM), age-based maintenance and overhauls to be done at fixed time interval, may fail to monitor the exact condition of the component. Thus, a progressive maintenance policy (PMP) may be more appropriate for the industries that deal with large, complex and critical repairable systems (RS) such as aerospace industries, nuclear power plants, etc.Design/methodology/approachA progressive maintenance policy is developed, in which hard life, PM scheduled time and overhaul period of the system are revised after each service activity by adjusting PM interval and mean residual life (MRL) such that the risk of failure is not increased.FindingsA comparative study is then carried out between the classic PM policy and developed PMP, and the improvement in availability, mean time between failures and reduction in maintenance cost is registered.Originality/valueThe proposed PMP takes care of the equipment degradation more efficiently than any other existing maintenance policies and is also flexible in its application as the policy can be continuously amended as per the failure profile of the equipment. Similar maintenance policies assuming lifetime distributions are available in the literature, but to ascertain that the proposed PMP is more suitable and applicable to the industries, this paper uses Kijima-based imperfect maintenance models. The proposed PMP is demonstrated through a real-time data set example.

Asymptotic failure rate of a Markov deteriorating system with preventive maintenance

2003 ◽  
Vol 40 (01) ◽  
pp. 1-19 ◽  
Author(s):  
Sophie Mercier ◽  
Michel Roussignol
Keyword(s):  
Failure Rate ◽  
Preventive Maintenance ◽  
Sufficient Conditions ◽  
Maintenance Policy ◽  
System A ◽  
Up States ◽  
Finite State ◽  
Degradation Degree ◽  
Preventive Maintenance Policy ◽  
Finite State Space

We consider a system with a finite state space subject to continuous-time Markovian deterioration while running that leads to failure. Failures are instantaneously detected. This system is submitted to sequential checking and preventive maintenance: up states are divided into ‘good’ and ‘degraded’ ones and the system is sequentially checked through perfect and instantaneous inspections until it is found in a degraded up state and stopped to allow maintenance (or until it fails). Time between inspections is random and is chosen at each inspection according to the current degradation degree of the system. Markov renewal equations fulfilled by the reliability of the maintained system are given and an exponential equivalent is derived for the reliability. We prove the existence of an asymptotic failure rate for the maintained system, which we are able to compute. Sufficient conditions are given for the preventive maintenance policy to improve the reliability and the asymptotic failure rate of the system. A numerical example illustrates our study.

scholarly journals Integrating Modelling of Maintenance Policies within a Stochastic Hybrid Automaton Framework of Dynamic Reliability

2021 ◽  
Vol 11 (5) ◽  
pp. 2300
Author(s):  
Simone Arena ◽  
Irene Roda ◽  
Ferdinando Chiacchio
Keyword(s):  
Model Systems ◽  
Mitigation Measures ◽  
Repairable Systems ◽  
Maintenance Policy ◽  
Hybrid Automaton ◽  
Dynamic Reliability ◽  
Software Model ◽  
Maintenance Policies ◽  
Mathematical Techniques ◽  
Continuous And Discrete Variables

The dependability assessment is a crucial activity for determining the availability, safety and maintainability of a system and establishing the best mitigation measures to prevent serious flaws and process interruptions. One of the most promising methodologies for the analysis of complex systems is Dynamic Reliability (also known as DPRA) with models that define explicitly the interactions between components and variables. Among the mathematical techniques of DPRA, Stochastic Hybrid Automaton (SHA) has been used to model systems characterized by continuous and discrete variables. Recently, a DPRA-oriented SHA modelling formalism, known as Stochastic Hybrid Fault Tree Automaton (SHyFTA), has been formalized together with a software library (SHyFTOO) that simplifies the resolution of complex models. At the state of the art, SHyFTOO allows analyzing the dependability of multistate repairable systems characterized by a reactive maintenance policy. Exploiting the flexibility of SHyFTA, this paper aims to extend the tools’ functionalities to other well-known maintenance policies. To achieve this goal, the main features of the preventive, risk-based and condition-based maintenance policies will be analyzed and used to design a software model to integrate into the SHyFTOO. Finally, a case study to test and compare the results of the different maintenance policies will be illustrated.

An Overview of the Recommender System

2013 ◽  
Vol 302 ◽  
pp. 787-791
Author(s):  
Lu Zhao ◽  
Rong Rong Yang ◽  
Meng Zhai ◽  
Feng Ming Liu
Keyword(s):  
Recommender System ◽  
System A ◽  
The Future

Delivering recommendation services are the trend of the future, so Recommender System varied very vital and widely applied in e-commerce websites to help customers in finding the items they want. A recommender system should be able to provide users with useful information about the items that might be interesting to them. The ability of immediately responding to changes in users preferences is a valuable asset for such systems. In recommender system, a variety of methods have been emerged as the basis for recommender. However, existing recommendation methods have the limitation. To overcome this limitation, we will propose new recommender system by combining the existing techniques. So, we firstly give an overview of recommender system for the future researches.

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