Time and magnitude monitoring based on the renewal reward process

2018 ◽  
Vol 179 ◽  
pp. 97-107 ◽  
Author(s):  
Sajid Ali ◽  
Antonio Pievatolo
2013 ◽  
Vol 26 (1) ◽  
pp. 108-112 ◽  
Author(s):  
Christophette Blanchet-Scalliet ◽  
Diana Dorobantu ◽  
Didier Rullière

1996 ◽  
Vol 33 (04) ◽  
pp. 1018-1032 ◽  
Author(s):  
Angelos Dassios

The distribution of the sample quantiles of random processes is important for the pricing of some of the so-called financial ‘look-back' options. In this paper a representation of the distribution of the α-quantile of an additive renewal reward process is obtained as the sum of the supremum and the infimum of two rescaled independent copies of the process. This representation has already been proved for processes with stationary and independent increments. As an example, the distribution of the α-quantile of a randomly observed Brownian motion is obtained.


1989 ◽  
Vol 3 (3) ◽  
pp. 393-396 ◽  
Author(s):  
J. M. McNamara

We consider a renewal reward process in continuous time. The supremum average reward, γ* for this process can be characterised as the unique root of a certain function. We show how one can apply the Newton–Raphson algorithm to obtain successive approximations to γ*, and show that the successive approximations so obtained are the same as those obtained by using the policy improvement technique.


2007 ◽  
Vol 6 (3) ◽  
pp. 279-295 ◽  
Author(s):  
Ruiqing Zhao ◽  
Wansheng Tang ◽  
Cheng Wang

2020 ◽  
Vol 44 (4) ◽  
pp. 1250-1262
Author(s):  
Aslı BEKTAŞ KAMIŞLIK ◽  
Büşra ALAKOÇ ◽  
Tülay KESEMEN ◽  
Tahir KHANİYEV

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chunxiao Zhang ◽  
Xinwang Li ◽  
Xiaona Liu ◽  
Qiang Li ◽  
Yizhou Bai

PurposeThe purpose of this paper is to focus on an optimizing maintenance policy with repair limit time for a new type of aircraft component, in which the lifetime is assumed to be an uncertain variable due to no historical operation data, and the repair time is a random variable that can be described by the experimental data.Design/methodology/approachTo describe this repair limit time policy over an infinite time horizon, an extended uncertain random renewal reward theorem is firstly proposed based on chance theory, involves uncertain random interarrival times and stochastic rewards. Accordingly, the uncertain random programming model, which minimized the expected maintenance cost rate, is formulated to find the optimal repair limit time.FindingsA numerical example with sensitivity analysis is provided to illustrate the utility of the proposed policy. It provides a useful reference and guidance for aircraft optimization. For maintainers, it plays an important guiding role in engineering practice.Originality/valueThe proposed uncertain random renewal reward process proved useful for the optimization of maintenance strategy with maintenance limited time for a new type of aircraft components, which provides scientific support for aircraft maintenance decision-making for civil aviation enterprises.


1996 ◽  
Vol 33 (4) ◽  
pp. 1018-1032 ◽  
Author(s):  
Angelos Dassios

The distribution of the sample quantiles of random processes is important for the pricing of some of the so-called financial ‘look-back' options. In this paper a representation of the distribution of the α-quantile of an additive renewal reward process is obtained as the sum of the supremum and the infimum of two rescaled independent copies of the process. This representation has already been proved for processes with stationary and independent increments. As an example, the distribution of the α-quantile of a randomly observed Brownian motion is obtained.


2000 ◽  
Vol 14 (4) ◽  
pp. 485-510
Author(s):  
Jie Mi

In a renewal process, the interarrival times are independent and identically distributed, and in a renewal reward process, the pairs of reward and interarrival time are i.i.d. Many useful results hold for these processes. This paper relaxes the assumption of identical distributions while keeping the assumption of independence. This paper explores the properties of the mean average number of occurrence of events and the mean average reward on any finite time interval. The paper also discusses the limiting properties of these two quantities and extends many results from the renewal process and renewal reward process to the more general counting process and reward sequence.


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