Reliability evaluation and big data analytics architecture for a stochastic flow network with time attribute

Chang, Ping Chen

doi:10.1007/s10479-019-03427-4

Cited by 12 publications

(6 citation statements)

References 37 publications

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“…In existing studies, reliability evaluation is mainly performed based on failure data or simulation. The evaluation method based on failure data is the earliest and most commonly used at present [1][2][3]. Failure data refer to the record data generated during development, testing, or use, including the failure mode, failure type, cause of failure, impact on production and occurrence time, etc., which describe the key features of fault events.…”

Section: Introductionmentioning

confidence: 99%

A Statistical Evaluation Method Based on Fuzzy Failure Data for Multi-State Equipment Reliability

Xu,

Yan,

Pei

et al. 2024

Mathematics

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For complex equipment, it is easy to over-evaluate the impact of failure on production by estimating the reliability level only through failure probability. To remedy this problem, this paper proposes a statistical evaluation method based on fuzzy failure data considering the multi-state characteristics of equipment failures. In this method, the new reliability-evaluation scheme is firstly presented based on the traditional statistical analysis method using the Weibull distribution function. For this scheme, the failure-grade index is defined, and a fuzzy-evaluation method is also proposed by comprehensively considering failure severity, failure maintenance, time, and cost; this is then combined with the time between failures to characterize the failure state. Based on the fuzzy failure data, an improved adaptive-failure small-sample-expansion method is proposed based on the classical bootstrap method and the deviation judgment between distributions of the original and newborn samples. Finally, a novel reliability-evaluation model, related to the failure grade and its membership degree, is established to quantify the reliability level of equipment more realistically. Example cases for three methods of the scheme (the failure-grade fuzzy-evaluation method, the sample-expansion method, and the reliability-evaluation modeling method) are presented, respectively, to validate the effectiveness and significance of the proposed reliability-evaluation technology.

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Section: Introductionmentioning

confidence: 99%

A Statistical Evaluation Method Based on Fuzzy Failure Data for Multi-State Equipment Reliability

Xu,

Yan,

Pei

et al. 2024

Mathematics

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show abstract

“…However, when conducting system analysis, a supervisor may be more interested in understanding the changes in network reliability for a fixed demand rate as time progresses. [2][3][4][5][6] Chang's study 11 identified the opportunity to connect classical and network reliability theory in the context of CFN, yet this past study focused on transmitting a specified demand within a fixed time constraint. To monitor the system and guarantee a certain service level as time varies, changes to network reliability are also worth studying in order to assess demand satisfaction.…”

Section: Introductionmentioning

confidence: 99%

“…However, this work was restricted to a network composed of binary‐state subsystems. Recently, Chang 11 proposed a methodology to analyze the time‐varying reliability of a CFN with the Weibull reliability function. An important finding 11 was that the reliability of a CFN decreases as the time constraint decreases, since a stricter time constraint is more difficult to satisfy.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Chang 11 proposed a methodology to analyze the time‐varying reliability of a CFN with the Weibull reliability function. An important finding 11 was that the reliability of a CFN decreases as the time constraint decreases, since a stricter time constraint is more difficult to satisfy. However, when conducting system analysis, a supervisor may be more interested in understanding the changes in network reliability for a fixed demand rate as time progresses 2–6 …”

Section: Introductionmentioning

confidence: 99%

“…Chang's study 11 identified the opportunity to connect classical and network reliability theory in the context of CFN, yet this past study focused on transmitting a specified demand within a fixed time constraint. To monitor the system and guarantee a certain service level as time varies, changes to network reliability are also worth studying in order to assess demand satisfaction.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Method for integrating classical and network reliability: A time varying network reliability evaluation

Chang

Huang

Fiondella

et al. 2021

Quality & Reliability Eng

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Many capacitated flow network papers treat reliability as a time‐invariant property. However, network components degrade over time suggesting that methods to quantify the time‐varying reliability of a network are appropriate. This paper proposes a method to compute the reliability of a multistate capacitated flow network in terms of multistate components characterized by a life distribution. The approach enables the calculation of the probability that demand can be satisfied over period of time despite component degradation. Examples are provided to clarify the steps required and to illustrate the assessments enabled. Based on the classical reliability theory, the Weibull reliability function is employed to characterize the degradation of network components. However, the approach is general and other distributions are also possible. The results indicate that the approach can assist a network supervisor determine reasonable demand rate and time constraint to satisfy demand as well as reason about alternative reliability improvement strategies.

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Reliability of spare routing via intersectional minimal paths within budget and time constraints by simulation

2021

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Reliability evaluation and big data analytics architecture for a stochastic flow network with time attribute

Cited by 12 publications

References 37 publications

A Statistical Evaluation Method Based on Fuzzy Failure Data for Multi-State Equipment Reliability

A Statistical Evaluation Method Based on Fuzzy Failure Data for Multi-State Equipment Reliability

Method for integrating classical and network reliability: A time varying network reliability evaluation

Reliability of spare routing via intersectional minimal paths within budget and time constraints by simulation

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