Extreme shock model with change point based on the Poisson process of shocks

Dheeraj Goyal; Min Xie

doi:10.1002/asmb.2881

Extreme shock model with change point based on the Poisson process of shocks

Dheeraj Goyal^*, Min Xie

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

Abstract

In this article, we introduce and study an extreme shock model in which the distribution of magnitude of shocks can change due to environmental effects. A new decision parameter is used to model the change point, and the non-homogeneous Poisson process is employed to model the arrival of shocks. We derive the reliability function and mean time to system failure for the defined model. Furthermore, we propose an optimal age replacement policy. The results are illustrated when the change point follows the Erlang distribution. © 2024 John Wiley & Sons Ltd.

Original language	English
Pages (from-to)	1635-1650
Journal	Applied Stochastic Models in Business and Industry
Volume	40
Issue number	6
Online published	15 Jun 2024
DOIs	https://doi.org/10.1002/asmb.2881
Publication status	Published - Nov 2024

Funding

The authors are thankful to the Editor-in-Chief, the Associate Editor and the anonymous Reviewers for their valuable constructive comments/suggestions which led to an improved version of the manuscript. This work is supported by National Natural Science Foundation of China (72371215) and by Research Grant Council of Hong Kong (11200621, 11201023). It is also funded by Hong Kong Innovation and Technology Commission (InnoHK Project CIMDA) and by the International Science and Technology Cooperation Program of Guangdong Province (Project #2022A0505050047).

Research Keywords

age replacement policy
change point
extreme shock model
non-homogeneous Poisson process
reliability

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10.1002/asmb.2881

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title = "Extreme shock model with change point based on the Poisson process of shocks",

abstract = "In this article, we introduce and study an extreme shock model in which the distribution of magnitude of shocks can change due to environmental effects. A new decision parameter is used to model the change point, and the non-homogeneous Poisson process is employed to model the arrival of shocks. We derive the reliability function and mean time to system failure for the defined model. Furthermore, we propose an optimal age replacement policy. The results are illustrated when the change point follows the Erlang distribution. {\textcopyright} 2024 John Wiley & Sons Ltd.",

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AU - Goyal, Dheeraj

AU - Xie, Min

PY - 2024/11

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AB - In this article, we introduce and study an extreme shock model in which the distribution of magnitude of shocks can change due to environmental effects. A new decision parameter is used to model the change point, and the non-homogeneous Poisson process is employed to model the arrival of shocks. We derive the reliability function and mean time to system failure for the defined model. Furthermore, we propose an optimal age replacement policy. The results are illustrated when the change point follows the Erlang distribution. © 2024 John Wiley & Sons Ltd.

KW - age replacement policy

KW - change point

KW - extreme shock model

KW - non-homogeneous Poisson process

KW - reliability

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DO - 10.1002/asmb.2881

M3 - RGC 21 - Publication in refereed journal

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SP - 1635

EP - 1650

JO - Applied Stochastic Models in Business and Industry

JF - Applied Stochastic Models in Business and Industry

IS - 6

ER -

Extreme shock model with change point based on the Poisson process of shocks

Abstract

Funding

Research Keywords

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GRF: Intelligent Prognostics and Health Management of Modular Systems

GRF: New Approaches for Reliability Analysis of Industrial Systems Subject to Multivariate Degradation

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Extreme shock model with change point based on the Poisson process of shocks

Abstract

Funding

Research Keywords

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Other Access Options

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GRF: Intelligent Prognostics and Health Management of Modular Systems

GRF: New Approaches for Reliability Analysis of Industrial Systems Subject to Multivariate Degradation

Cite this