Age replacement policy in a random environment using systemability

Persona, Alessandro; Pham, Hoang; Sgarbossa, Fabio

doi:10.1080/00207720903353617

Cited by 16 publications

(13 citation statements)

References 33 publications

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“…This research differs from based on the analysed preventive maintenance policy. As indicated by Barlow & Hunter, the PRP, called Type II, here investigated, is applied in maintaining large, complex systems while age replacement policy, defined as Type I, is most useful to maintain simple equipment.…”

Section: Introductionmentioning

confidence: 89%

“…This research applies the concept of systemability to the periodic replacement policy (PRP), in order to demonstrate the importance of considering the operating conditions during the design of optimal maintenance policy, and quantify the environmental impacts of such policy as a follow‐up of a previous paper developed by the authors …”

Section: Introductionmentioning

confidence: 99%

“…As a consequence, the application context and the based assumptions of the problem are different. Moreover, the analytical procedures used in the research and the final mathematical formulations changed from the previous study carried out by the author . Other relevant difference regards the introduction of non‐homogenous Poisson process (NHPP) for modelling the sequence of failure at a system level.…”

Section: Introductionmentioning

confidence: 99%

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Using Systemability Function for Periodic Replacement Policy in Real Environments

Sgarbossa

Persona

Pham

2014

Quality & Reliability Eng

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In the last decades, various maintenance policies have been developed and widely used, including, but not limited to replacement policy and preventive maintenance policy, depending on an accurate estimation of the reliability and the failure intensity functions. Many studies, yet, haven't considered the environmental factors (EFs) and their effects on the survival distribution of operating units. This paper is a following up with our recent research about environmental impacts on preventive maintenance by investigating the periodic replacement policy using a systemability approach. The differences between the classical maintenance approach and the systemability approach have been investigated and applied to a real industrial setting to evaluate the importance and the relevance of taking into account EFs in the implementation of one maintenance policy versus another

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Section: Introductionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using Systemability Function for Periodic Replacement Policy in Real Environments

Sgarbossa

Persona

Pham

2014

Quality & Reliability Eng

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“…Shock models have been widely studied by Chen and Li (2008), Mallor and Santos (2003), Mallor and Santos (2003), Mallor et al (2006), Gut (2001), Li (1984), Zehui and Kong (2007), Zehui and Zhao (2007), Bai et al (2006), Persona et al (2010), Sgarbossa and Pham (2010), Pham (2005), and Pham and Wang (1996) in order to provide mathematical formulations for modeling the system reliability in random environments. Traditionally, three principal models are considered: cumulative shock model, extreme shock models and run shock model.…”

Section: Random Shock Modelmentioning

confidence: 99%

Imperfect preventive maintenance policies for two-process cumulative damage model of degradation and random shocks

Wang¹,

Pham²

2011

Int J Syst Assur Eng Manag

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In numerous applications, the system may fail due to multiple competing risks, especially degradation processes and random shocks. In this paper, we develop a two-process combination model for degraded system subject to cumulative effect from random shocks and degradation with two kinds of path function, including additive and multiplicative. Two numerical examples with sensitivity analysis for additive and multiplicative degradation path are discussed separately to illustrate this combination model. Based on the definition of the cumulative damage system, the imperfect preventive maintenance policy (N*, T*) with a common improvement factor is obtained to minimize the expected maintenance cost rate. The uncertainty of the reliability estimation and maintenance optimization is considered in the problem modeling. Also some extensions for this combination model are suggested for further research.

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“…Teng and Pham [3] discussed a generalized model that captures the uncertainty of the environment and its effects upon the software failure rate. Other researchers [8,[19][20][21]24,28] have also developed reliability and cost models incorporating both testing phase and operating phase in the software development cycle for estimating the reliability of software systems in the field. Pham et al [26] recently discussed a new logistic software reliability model where the fault-detection rate per unit time follows a three-parameter logistic function.…”

Section: Introductionmentioning

confidence: 99%

A generalized fault-detection software reliability model subject to random operating environments

Pham

2016

Vietnam J Comput Sci

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Many software reliability growth models (SRGMs) have been developed in the past three decades to estimate software reliability measures such as the number of remaining faults and software reliability. The underlying common assumption of many existing models is that the operating environment and the developing environment are the same. This is often not the case in practice because the operating environments are usually unknown due to the uncertainty of environments in the field. In this paper, we develop a generalized software reliability model incorporating the uncertainty of fault-detection rate per unit of time in the operating environments. A logistic fault-detection software reliability model is derived. Examples are included to illustrate the goodness of fit of the proposed model and existing nonhomogeneous Poisson process (NHPP) models based on a set of failure data. Three goodness-of-fit criteria, such as mean square error, predictive power, and predictive ratio risk are used as an example to illustrate model comparisons. The results show that the proposed logistic fault-detection model fit significantly better than other existing NHPP models based on all three goodness-of-fit criteria.

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Age replacement policy in a random environment using systemability

Cited by 16 publications

References 33 publications

Using Systemability Function for Periodic Replacement Policy in Real Environments

Using Systemability Function for Periodic Replacement Policy in Real Environments

Imperfect preventive maintenance policies for two-process cumulative damage model of degradation and random shocks

A generalized fault-detection software reliability model subject to random operating environments

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