Probabilistic Fault Diagnosis of Safety Instrumented Systems based on Fault Tree Analysis and Bayesian Network

Chiremsel, Zakarya; Said, Rachid Nait; Chiremsel, Rachid

doi:10.1007/s11668-016-0140-z

Cited by 38 publications

(15 citation statements)

References 18 publications

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“…Probabilities can be inserted into the BN in order to find the probability of the outcome; same as FT. The flexibility of BN structure and its probabilistic reasoning engine has enough capability for risk analysis in the large and complex systems [51,57,58,[69][70][71][72][73][74]. In a BN model, both forward and backward analysis could be performed.…”

Section: Bayesian Updating Mechanismmentioning

confidence: 99%

Knowledge acquisition development in failure diagnosis analysis as an interactive approach

Yazdi

Soltanali

2018

Int J Interact Des Manuf

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Safety and reliability analysis is an important issue to prevent an event which may to occurrence of catastrophic accident in process industries. In this context, conventional safety and reliability assessment technique like as fault tree analysis have been widely used in this regards; however, they still suffer in subjective uncertainty processing and dynamic structure representation which are important in risk assessment procedure. In this paper, a new framework based on 2-tuple intuitionistic fuzzy numbers and Bayesian network mechanism is proposed to evaluate system reliability, to deal with mentioned drawbacks, and to recognize the most critical system components which affects the system reliability. The reliability and safety guarantee of such system in the aspect of continuity operations and enhancing the safety of operators and vehicle drivers are crucial. The results revealed that the proposed model could be useful for diagnosing the systems' faults compared with listing approaches of safety and reliability analysis.

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Section: Bayesian Updating Mechanismmentioning

confidence: 99%

Knowledge acquisition development in failure diagnosis analysis as an interactive approach

Yazdi

Soltanali

2018

Int J Interact Des Manuf

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“…The development of instrumentation and automation for modern industrial processes in the chemical and general manufacturing industries allows large quantities of data to be utilized for assessing current operating conditions (Kruger & Xie, 2012;Severson, Chaiwatanodom & Braatz, 2016). Traditional approaches to monitor general processes include model-based (Ding, 2013;Zhong, Xue & Ding, 2018;Liu, Luo, Yang & Wu, 2016;Li, Gao, Shi & Lam, 2016;Zhao, Yang, Ding & Li, 2018), signal-based (Lei, Lin, He & Zuo, 2013;Yan, Gao & Chen, 2014;Fan, Cai, Zhu, Shen, Huang & Shang, 2015;Wu, Guo, Xie, Ni & Liu, 2018), and knowledge-based (Gao, Cecati & Ding, 2015;Mohammadi & Montazeri-Gh, 2015;Chiremsel,  Corresponding Authors: +86-25-8489-3221, q.chen@nuaa.edu.cn (Qian Chen); +1-518-276-4818, krugeu@rpi.edu (Uwe Kruger) Said & Chiremsel, 2016;Davies, Jackson & Dunnett, 2017) techniques. Based on their conceptual simplicity, techniques that relate to multivariate statistical process control (MSPC) (Kruger & Xie, 2012;Qin, 2012;Ge, Song & Gao, 2013;Yin, Li, Gao & Kaynak, 2015) have also gained attention over the past few decades, particularly for applications to industrial processes that produce larger variable sets.…”

Section: Introductionmentioning

confidence: 99%

Monitoring nonstationary and dynamic trends for practical process fault diagnosis

Lin

Krüger

et al. 2019

Control Engineering Practice

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This article introduces a revised common trend framework to monitor nonstationary and dynamic trends in industrial processes and shows needs for each improvement on the basis of three application studies. These improvements relate to (i) the extension of the common trend framework to include sets that contain stationary and nonstationary variables, (ii) handling cases where residuals are not drawn from multivariate normal distributions and (iii) the application of the framework to larger variable sets. Existing work does not adequately address these practically important issues. Industrial application studies highlight the needs for (i) the extended framework to model data sets containing stationary and nonstationary variables, (ii) handling statistics that are not based on normally distributed residuals and (iii) the use of Chigira procedure to robustly extract common trends. The extended framework is compared to traditional approaches. Keywords common trends models; Kasa decomposition; cointegration; latent stationary and nonstationary factors; generalized data structure; forecast recovery; multivariate normal distribution processes has been presented through the application to a glass melter process (Lin, Kruger & Chen, 2017), the following practically relevant issues have not been considered: the application studies presented herein contain recorded variable sets that are both, nonstationary and stationary, whereas Lin, Kruger & Chen (2017) addressed the problem of variable sets that are nonstationary only;  the model residuals may not be drawn from multivariate normal distributions, in contrast to the work in Lin, Kruger & Chen (2017) where this assumption is made; and  the conventional cointegration testing procedure by Johansen (1995) may run into difficulties if the number of variables is large, which is typically the case for complex industrial processes.This article is divided into the following sections. Section 2 gives a detailed description of the processes, including the FCCU simulator, the polymerization and the distillation process, used in this article. Section 3 then summarizes the monitoring framework and introduces an improved common trends framework. Sections 4 to 6 summarize the applications to the three processes. A concluding summary is given in Section 7. Process DescriptionsThis section provides a detailed description of the three processes that are used to demonstrate the working of the improved cointegration framework. Subsection 2.1 gives a description of the simulated fluid catalytic cracking unit. Subsections 2.2 and 2.3 then provide details of the polymerization and distillation processes, respectively. Fluid Catalytic Cracking UnitA fluid catalytic cracking unit (FCCU) is an important part of a refinery. An FCCU receives several different heavy feed stocks from other refinery units and cracks these into more valuable components that are usually blended into gasoline and other products. The main feed stream to an FCCU is gas oil, but heavier diesel and wash oil streams also cont...

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“…Meanwhile, there is an indeterminate coupling relationship between subsystems and subsystems, or between units and units, thus forming a complex coupling propagation process, which brings certain difficulties to product design engineers. Commonly used reliability analysis methods include FMEA [1][2][3], FTA [4,5], Bayesian [6,7], Markov [8,9], and Petri net [10,11]. These methods mainly judge fault behavior based on single index such as failure rate, risk value, and fault propagation probability, and they analyze each event as an independent event.…”

Section: Introductionmentioning

confidence: 99%

Risk Analysis of Coupling Fault Propagation Based on Meta‐Action for Computerized Numerical Control (CNC) Machine Tool

2019

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A comprehensive fault analysis of CNC machine tool is conducive to improving its reliability. Due to the highly complex structure of CNC machine tool, there are different degrees of coupling relationship between faults. However, the traditional fault analysis methods (FMEA, FTA, etc.) for CNC machine tool do not solve this problem perfectly. Therefore, we propose a coupling fault propagation model based on meta-action. First, in order to simplify the structural complexity of CNC machine tool, the “Function-Motion-Action (FMA)” decomposition structure is used to decompose the product function into simple meta-action, and the numerical matrix is used to quantify the coupling relationship between the meta-actions. Then, based on the fault transfer characteristics of meta-action, the fault propagation model is established, and the global risk effect (GRE) is combined to realize the comprehensive evaluation of the risk criticality of meta-actions. Finally, the rationality and validity of the method are verified by the case analysis of the automatic pallet changer (APC) of computerized numerical control (CNC) machining center.

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Probabilistic Fault Diagnosis of Safety Instrumented Systems based on Fault Tree Analysis and Bayesian Network

Cited by 38 publications

References 18 publications

Knowledge acquisition development in failure diagnosis analysis as an interactive approach

Knowledge acquisition development in failure diagnosis analysis as an interactive approach

Monitoring nonstationary and dynamic trends for practical process fault diagnosis

Risk Analysis of Coupling Fault Propagation Based on Meta‐Action for Computerized Numerical Control (CNC) Machine Tool

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