The propagation of faults in process plants: 6, overview of, and modelling for, fault tree synthesis

Hunt, Andrew; Kelly, B.E.; Mullhi, J.S.; Lees, F.P.; Rushton, A.G.

doi:10.1016/0951-8320(93)90041-v

Cited by 27 publications

(6 citation statements)

References 41 publications

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“…Automotive body and its assembly fixture: (a) body coordinate system; (b) 3-2-1 fixturing principle for rigid part nents and their interactions is known [9][10][11][12]. However, these analysis methods cannot be applied directly to manufacturing-system reliability analysis because product-quality information and the interdependency between part quality and manufacturing-system component reliability are not considered.…”

Section: The Effect Of Fixture Functionality On Product Qualitymentioning

confidence: 98%

Quality and reliability information integration for design evaluation of fixture system reliability

Jin

Chen

2001

Quality & Reliability Eng

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SUMMARYQuality and reliability are two important factors in manufacturing-system design. However, the analysis and optimization of manufacturing-system reliability and product quality are normally conducted separately in practice. There is no general framework to integrate these two important factors, quantitatively analyze the interactions between them, and further study their integrated effects on the manufacturing-system performance. In this paper, the QR-Co-Effect of product/part quality and manufacturing-system component reliability is investigated in an assembly fixture system. The concept, model and analysis of QR-Co-Effect are addressed in this paper.

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Section: The Effect Of Fixture Functionality On Product Qualitymentioning

confidence: 98%

Quality and reliability information integration for design evaluation of fixture system reliability

Jin

Chen

2001

Quality & Reliability Eng

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“…An important challenge in FTA is the creation of faithful FT models. Therefore, inference of FTs, also known as construction [24], synthesis [8], or induction [16], has been investigated since the 1970s. Three categories of approaches exist: (i) Knowledge-based methods were investigated first, and are semiautomated approaches that derives an FT from a knowledge-based representation using heuristics [3].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularization

Jimenez-Roa

Volk

Stoelinga

2022

Lecture Notes in Computer Science

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We present SymLearn, a method to automatically infer fault tree (FT) models from data. SymLearn takes as input failure data of the system components and exploits evolutionary algorithms to learn a compact FT matching the input data. SymLearn achieves scalability by leveraging two common phenomena in FTs: (i) We automatically identify symmetries in the failure data set, learning symmetric FT parts only once. (ii) We partition the input data into independent modules, subdividing the inference problem into smaller parts.We validate our approach via case studies, including several truss systems, which are symmetric structures commonly found in infrastructures, such as bridges. Our experiments show that, in most cases, the exploitation of modules and symmetries accelerates the FT inference from hours to under three minutes. This research has been partially funded by NWO under the grant PrimaVera number NWA.1160.18.238 and by the ERC Consolidator grant CAESAR number 864075.

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“…These include the use of first‐principle models to identify (a) equipment items that cause hazardous situations and (b) hazardous situations that lead to potential catastrophic accidents—and the use of dynamic risk analysis, which has advanced in recent years. For example, copulas have been used to capture low‐probability safety system failures and rare events; bow‐tie, fault‐tree, and event‐tree analyses along with fault‐detection techniques have been used; and bifurcation analyses have been performed . Hazard and operability (HAZOP) studies, which allow for qualitative process safety analysis, have been conducted extensively .…”

Section: Introductionmentioning

confidence: 99%

Model‐predictive safety system for proactive detection of operation hazards

Ahooyi¹,

Soroush²,

Arbogast

et al. 2016

AIChE Journal

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A method of designing model-predictive safety systems that can detect operation hazards proactively is presented. Such a proactive safety system has two major components: a set of operability constraints and a robust state estimator. The safety system triggers alarm(s) in real time when the process is unable to satisfy an operability constraint over a receding time-horizon into the future. In other words, the system uses a process model to project the process operability status and to generate alarm signals indicating the presence of a present or future operation hazard. Unlike typical existing safety systems, it systematically accounts for nonlinearities and interactions among process variables to generate alarm signals; it provides alarm signals tied to unmeasurable, but detectable, state variables; and it generates alarm signals before an actual operation hazard occurs. The application and performance of the method are shown using a polymerization reactor example.

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The propagation of faults in process plants: 6, overview of, and modelling for, fault tree synthesis

Cited by 27 publications

References 41 publications

Quality and reliability information integration for design evaluation of fixture system reliability

Quality and reliability information integration for design evaluation of fixture system reliability

Data-Driven Inference of Fault Tree Models Exploiting Symmetry and Modularization

Model‐predictive safety system for proactive detection of operation hazards

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