Reliability of manufacturing equipment in complex environments

Kharoufeh, Jeffrey P.; Cox, Steven M.; Oxley, Mark E.

doi:10.1007/s10479-011-0839-x

Cited by 30 publications

(16 citation statements)

References 37 publications

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“…γ is the initial probability vector of the system. γG t 1 is a probability vector and shows the probability of being at each state at time t 1…”

Section: Multi-point Availability Of the Systemmentioning

confidence: 99%

“…where n = 3, p (1,1) = 0.95 , p (1,2) = 0.93 , p (2,1) = 0.98, p (2,2) = 0.96, p (3,1) = 0.9, p ( For comparison, we first analyze the system reliability when the impact of the environment is neglected. Assume the system works in a fixed environment (high temperature and low pressure) (a = 1, α = 2).…”

Section: Numerical Examplesmentioning

confidence: 99%

“…It is well known that many factors can influence the rate at which machine tools degrade. Among them, the environment of the manufacturing equipment can significantly impact the system operation . For instance, significant fluctuations in the ambient temperature or humidity can cause workpiece expansion and contraction and even premature corrosion.…”

Section: Introductionmentioning

confidence: 99%

“…Among them, the environment of the manufacturing equipment can significantly impact the system operation. 1 For instance, significant fluctuations in the ambient temperature or humidity can cause workpiece expansion and contraction and even premature corrosion. Motors can fail under vibrations of high intensity or other environments.…”

Section: Introductionmentioning

confidence: 99%

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Aggregated Markov‐based reliability analysis of multi‐state systems under combined dynamic environments

Jia

Xing

Song

2020

Quality & Reliability Eng

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Reliability of a system may differ greatly when operating under different environments. However, the existing works have either neglected the environment factor in system reliability analysis or considered this factor for binary systems or systems subject to a single environment (parameter). In this paper, we make contributions by modeling a multi‐state system operating under hybrid dynamic environments affected by multiple environmental parameters. Different Markov chains with finite states are used to represent the random system behavior and dynamic environments, leading to an aggregated Markov process that models the overall system behavior. An effective approach based on state partitions and aggregations is suggested for assessing the system reliability indexes, including reliability, availability, multi‐point availability, and environment‐based reliability. A high‐pressure homogenizer system is analyzed to demonstrate the proposed model and show the comparison of the reliability of system under fixed and dynamic environment.

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“…γ is the initial probability vector of the system. γG t 1 is a probability vector and shows the probability of being at each state at time t 1…”

Section: Multi-point Availability Of the Systemmentioning

confidence: 99%

Section: Numerical Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Aggregated Markov‐based reliability analysis of multi‐state systems under combined dynamic environments

Jia

Xing

Song

2020

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…A single-unit system which operates in a Markovian environment was investigated in [18] and the failure time distribution was derived. Kharoufeh et al [19] studied the reliability of manufacturing equipment in complex environments, where the environment was assumed to be a temporally nonhomogeneous continuous-time Markov chain and a temporally homogeneous semi-Markov process, respectively. The lifetime distribution was obtained in that paper.…”

Section: Introductionmentioning

confidence: 99%

Performance measures for systems under multiple environments

Liu

Cui

et al. 2016

IEEE/CAA J. Autom. Sinica

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In this paper, the system which operates in multiple environments is studied. The environment process is governed by a Markov process, and the deterioration process is governed by another Markov process given the system in a certain environment. In terms of the above two processes, a new Markov process is constructed to represent the evolution of the system. In terms of Ion-Channel modeling theory, Markov process theory and matrix partition method, some reliability indexes for the system are obtained, i.e., system reliability, environment reliability, system multiple-interval reliability, system availability, environment availability, system multiple-point availability, etc. Finally, a numerical example is given to illustrate the results obtained in the paper.

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Markov additive processes for degradation with jumps under dynamic environments

Yin

Feng

Kao

et al. 2021

Naval Research Logistics

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We use general Markov additive processes (Markov modulated Lévy processes) to integrally handle the complexity of degradation including internally‐induced and externally‐induced stochastic properties with complex jump mechanisms. The background component of the Markov additive process is a Markov chain defined on a finite state space; the additive component evolves as a Lévy subordinator under a certain background state, and may have instantaneous nonnegative jumps occurring at the time the background state switches. We derive the Fokker–Planck equations for such Markov modulated processes, based on which we derive Laplace expressions for reliability function and lifetime moments, represented by the infinitesimal generator matrices of Markov chain and the Lévy measure of Lévy subordinator. The superiority of our models is their flexibility in modeling degradation data with jumps under dynamic environments. Numerical experiments are used to demonstrate that our general models perform well.

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Reliability of manufacturing equipment in complex environments

Cited by 30 publications

References 37 publications

Aggregated Markov‐based reliability analysis of multi‐state systems under combined dynamic environments

Aggregated Markov‐based reliability analysis of multi‐state systems under combined dynamic environments

Performance measures for systems under multiple environments

Markov additive processes for degradation with jumps under dynamic environments

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