Reliability analysis of multi-state systems with s-dependent components

Cuong D. Dao; Ming J. Zuo

doi:10.1109/RAMS.2015.7105177

Reliability analysis of multi-state systems with s-dependent components

Cuong D. Dao, Ming J. Zuo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

9 Citations (Scopus)

Abstract

The assumption of stochastic independence between components is frequently made in studies of system reliability. However, in a specific system, the failure of a component can trigger the failure of other components, or the current health condition of a component may affect the performance of other components. Thus, the state of a component can affect the state and degradation of other components in a multi-component system, that is, stochastic dependence (s-dependence) may exist in real and complex systems. In this paper, reliability analysis of multi-state systems (MSS) with s-dependent components will be considered. The MSS consists of 2 multi-state components in series, with each component possibly operating at different performance levels, varying from perfect functioning to complete failure. When the first component degrades to a lower performance level, it affects the state as well as the degradation of the other component in the system. A combined technique of stochastic process and modified universal generating function is used to evaluate the system reliability. The combined approach is then verified using the Monte Carlo Simulation method. An illustrative example on reliability analysis of MSS with dependent components is also provided.

Original language	English
Title of host publication	2015 Annual Reliability and Maintainability Symposium (RAMS)
Number of pages	6
ISBN (Electronic)	978-1-4799-6703-2
DOIs	https://doi.org/10.1109/RAMS.2015.7105177
Publication status	Published - 2015
Externally published	Yes
Event	IEEE 61st 2015 Annual Reliability and Maintainability Symposium - Innisbrook resort and golf club, Palm Harbor, United States Duration: 26 Jan 2015 → 29 Jan 2015 Conference number: 61 https://www.ieee.org/conferences_events/conferences/conferencedetails/index.html?Conf_ID=15101

Conference

Conference	IEEE 61st 2015 Annual Reliability and Maintainability Symposium
Abbreviated title	RAMS 2015
Country	United States
City	Palm Harbor
Period	26/01/15 → 29/01/15
Internet address	https://www.ieee.org/conferences_events/conferences/conferencedetails/index.html?Conf_ID=15101

Access to Document

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title = "Reliability analysis of multi-state systems with s-dependent components",

abstract = "The assumption of stochastic independence between components is frequently made in studies of system reliability. However, in a specific system, the failure of a component can trigger the failure of other components, or the current health condition of a component may affect the performance of other components. Thus, the state of a component can affect the state and degradation of other components in a multi-component system, that is, stochastic dependence (s-dependence) may exist in real and complex systems. In this paper, reliability analysis of multi-state systems (MSS) with s-dependent components will be considered. The MSS consists of 2 multi-state components in series, with each component possibly operating at different performance levels, varying from perfect functioning to complete failure. When the first component degrades to a lower performance level, it affects the state as well as the degradation of the other component in the system. A combined technique of stochastic process and modified universal generating function is used to evaluate the system reliability. The combined approach is then verified using the Monte Carlo Simulation method. An illustrative example on reliability analysis of MSS with dependent components is also provided.",

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N2 - The assumption of stochastic independence between components is frequently made in studies of system reliability. However, in a specific system, the failure of a component can trigger the failure of other components, or the current health condition of a component may affect the performance of other components. Thus, the state of a component can affect the state and degradation of other components in a multi-component system, that is, stochastic dependence (s-dependence) may exist in real and complex systems. In this paper, reliability analysis of multi-state systems (MSS) with s-dependent components will be considered. The MSS consists of 2 multi-state components in series, with each component possibly operating at different performance levels, varying from perfect functioning to complete failure. When the first component degrades to a lower performance level, it affects the state as well as the degradation of the other component in the system. A combined technique of stochastic process and modified universal generating function is used to evaluate the system reliability. The combined approach is then verified using the Monte Carlo Simulation method. An illustrative example on reliability analysis of MSS with dependent components is also provided.

AB - The assumption of stochastic independence between components is frequently made in studies of system reliability. However, in a specific system, the failure of a component can trigger the failure of other components, or the current health condition of a component may affect the performance of other components. Thus, the state of a component can affect the state and degradation of other components in a multi-component system, that is, stochastic dependence (s-dependence) may exist in real and complex systems. In this paper, reliability analysis of multi-state systems (MSS) with s-dependent components will be considered. The MSS consists of 2 multi-state components in series, with each component possibly operating at different performance levels, varying from perfect functioning to complete failure. When the first component degrades to a lower performance level, it affects the state as well as the degradation of the other component in the system. A combined technique of stochastic process and modified universal generating function is used to evaluate the system reliability. The combined approach is then verified using the Monte Carlo Simulation method. An illustrative example on reliability analysis of MSS with dependent components is also provided.

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