Quantitative resilience assessment of chemical process systems using functional resonance analysis method and Dynamic Bayesian network

Zinetullina, Altyngul; Yang, Ming; Khakzad, Nima; Golman, Boris; Li, Xinhong

doi:10.1016/j.ress.2020.107232

Cited by 70 publications

(27 citation statements)

References 34 publications

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“…Although the character of the studies presented in this paper is purely qualitative, the adopted research design could be coupled with a semi-quantitative approach for variability modelling. Recent research on this topic has employed techniques such as Fuzzy Logic ( Slim and Nadeau, 2019 ; Hirose and Sawaragi, 2020 ), Monte Carlo simulation ( Patriarca et al, 2017a , Patriarca et al, 2017b ) and Bayesian networks ( Zinetullina et al, 2021 ). Drawing from these proposals, we believe that a good aim for complementing a FRAM prospective analysis of system functioning during crisis situations would be an approach towards reducing subjectivity in the estimation of changes in variability dynamics of directly impacted functions.…”

Section: Discussionmentioning

confidence: 99%

On the brink of disruption: Applying Resilience Engineering to anticipate system performance under crisis

et al. 2022

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Section: Discussionmentioning

confidence: 99%

On the brink of disruption: Applying Resilience Engineering to anticipate system performance under crisis

et al. 2022

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“…Salehi et al [8] modeled and analyzed hospital to home transition processes of frail older adults, in order to identify the challenges within the process. Zinetullina et al [9] integrated FRAM and dynamic Bayesian Network for quantitative resilience assessment of chemical process systems. Kaya et al [10] applied FRAM by integrating Monte Carlo simulations and a criticality matrix to explore how the system-based perspective would enrich the quantified risk-orientated analysis in a tram operating system.…”

Section: Related Workmentioning

confidence: 99%

Developing Reliable Digital Healthcare Service Using Semi-Quantitative Functional Resonance Analysis

Zhou¹,

Matsubara²,

Takada³

2023

Computer Systems Science and Engineering

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Since entering the era of Industry 4.0, the concept of Healthcare 4.0 has also been put forward and explored by researchers. How to use Information Technology (IT) to better serve people's healthcare is one of the most featured emerging directions in the academic circle. An important field of Healthcare 4.0 research is the reliability engineering of healthcare service. Because healthcare systems often affect the health and even life of their users, developers must be very cautious in the design, development, and operation of these healthcare systems and services. The problems to be solved include the reliability of business process, system functions, and personal healthcare data. The Functional Resonance Analysis Method (FRAM) has been applied in reliability engineering for safety-critical systems in available studies, using both qualitative and quantitative approaches. However, the method has not been applied in the field of digital healthcare services development. Therefore, to narrow the gap, we present in this paper a semi-quantitative functional resonance analysis method to develop reliable healthcare services for diabetics. Moreover, this paper has tried to improve the reliability design of the service-oriented architecture (SOA) of traditional insulin pump therapy by system thinking.

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“…Ba-Alawi et al [ 14 ] conducted a resilience assessment of the membrane bioreactor in a wastewater treatment plant based on the performance curves, with the assumption that the distribution of the failure data followed a lognormal distribution. Zinetullina et al [ 15 ] proposed a quantitative resilience assessment for chemical process systems with normally distributed variables such as timing and precision. On the other hand, some researchers analyzed the resilience laws of complex networks such as transportation systems, water supply systems, and electric power systems.…”

Section: Introductionmentioning

confidence: 99%

System resilience distribution identification and analysis based on performance processes after disruptions

Song

Li²

2022

PLoS ONE

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Resilience is a system’s ability to withstand a disruption and return to a normal state quickly. It is a random variable due to the randomness of both the disruption and resilience behavior of a system. The distribution characteristics of resilience are the basis for resilience design and analysis, such as test sample size determination and assessment model selection. In this paper, we propose a systematic resilience distribution identification and analysis (RDIA) method based on a system’s performance processes after disruptions. Typical performance degradation/recovery processes have linear, exponential, and trigonometric functions, and they have three key parameters: the maximum performance degradation, the degradation duration, and the recovery duration. Using the Monte Carlo method, these three key parameters are first sampled according to their corresponding probability density functions. Combining the sample results with the given performance function type, the system performance curves after disruptions can be obtained. Then the sample resilience is computed using a deterministic resilience measure and the resilience distribution can be determined through candidate distribution identification, parameter estimation, and a goodness-of-fit test. Finally, we apply our RDIA method to systems with typical performance processes, and both the orthogonal experiment method and the control variable method are used to investigate the resilience distribution laws. The results show that the resilience of these systems follows the Weibull distribution. An end-to-end communication system is also used to explain how to apply this method with simulation or test data in practice.

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Quantitative resilience assessment of chemical process systems using functional resonance analysis method and Dynamic Bayesian network

Cited by 70 publications

References 34 publications

On the brink of disruption: Applying Resilience Engineering to anticipate system performance under crisis

On the brink of disruption: Applying Resilience Engineering to anticipate system performance under crisis

Developing Reliable Digital Healthcare Service Using Semi-Quantitative Functional Resonance Analysis

System resilience distribution identification and analysis based on performance processes after disruptions

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