Monitoring of Complex Processes with Bayesian Networks

Verron, Sylvain; Tiplica, Téodor; Kobi, Abdessamad

doi:10.5772/10068

Cited by 8 publications

(7 citation statements)

References 32 publications

(19 reference statements)

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“…In these cases, Boolean or classic logic may be insufficient [6]. Such probabilistic events can be framed within Bayesian networks, which provide a theoretical context to represent events using discrete random variables [7]. The relationship between those events is then represented by conditional probabilities [6].…”

Section: Bayesian Networkmentioning

confidence: 99%

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Uncertainty in Building Inspection and Diagnosis: A Probabilistic Model Quantification

et al. 2021

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In the field of building inspection and diagnosis, uncertainty is common and surveyors are aware of it, although it is not easily measured. This research proposes a model to quantify uncertainty based on the inspection of rendered façades. A Bayesian network is developed, considering three levels of variables: characteristics of the building, façade and exposure conditions; causes of defects; and defects. To compute conditional probabilities, the results of an inspection campaign from the literature are used. Then, the proposed model is validated and verified using inspection results from another sample, the combination of a strength-of-influence diagram and sensitivity analysis and the application of the model to a case study. Results show that the probabilities computed by the model are a reasonable representation of the hesitancy in decision making during the diagnosis process based only on visual observation. For instance, design and execution errors show lower probabilities due to not being verifiable a posteriori without detailed documentation. The proposed model may be extended and replicated for other building materials in the future, as it may be a useful tool to improve the perception of uncertainty in a key stage of building maintenance or rehabilitation.

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Section: Bayesian Networkmentioning

confidence: 99%

“…After the identification of variables, the type and states of the nodes were determined. Nodes can be discrete or continuous [7]. Discrete random variables represented by chance nodes were included in the proposed BN model.…”

Section: Development Of the Model And Defining Cptsmentioning

confidence: 99%

Uncertainty in Building Inspection and Diagnosis: A Probabilistic Model Quantification

et al. 2021

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“…We can mention: Petri nets [2], Markov chains [5], dynamic Bayesian networks (DBN) [6], neural networks [4], etc.…”

Section: E Qualitative Modellingmentioning

confidence: 99%

“…by the existence of interactions between the various components that may be due in part to the integration of several technologies. Hybrid systems are systems characterized by the presence of continuous phenomena and discrete event [6]. Dynamical systems are characterized by functional relationships between components that constitute them.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a methodology for evaluation of the reliability of mechatronic systems

Hammouda

Habchi

Barthod

et al. 2014

2014 10th France-Japan/ 8th Europe-Asia Congress on Mecatronics (MECATRONICS2014- Tokyo)

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Reliability is one of the most important parameters of dependability. It is addressed whenever we want reliable, available and safe systems. Indeed, reliability acts on the availability and security of systems. It must be considered throughout the overall life cycle of the product or system (design, manufacturing, operation). The objective of this communication is to build an overall approach of reliability for mechatronic systems since they are complex and multi-technology integrating several properties such as: dynamic, hybrid, reconfigurable, and interactive.

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“…Recently these models have aroused much attention especially in the area of system biology [3], [10] and cognitive science [5]. Still in process engineering BN are only used for fault diagnosis [7] and monitoring [19] tasks but not for the detection of cause-effect relationships in data. For this reason we will examine the use of BN to detect unknown causal associations in measurement data coming from process factors.…”

Section: Introductionmentioning

confidence: 99%

Causal structure learning in process engineering using Bayes Nets and soft interventions

Kuehnert

Bernard

Frey

2011

2011 9th IEEE International Conference on Industrial Informatics

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As modern industrial processes become more and more complex, machine learning is increasingly used to gain additional knowledge about the process from production data. Up to now these methods are usually based on correlations between process parameters and hence cannot describe cause-effect relationships. To overcome this problem we propose a hybrid (constraint and scoring-based) structure learning method based on a Bayesian Network to detect the causal structure out of its data. Starting with an empty graph, first the underlying undirected graph is learned and edges coming from root nodes are directed using a constraint-based approach. Next a scoring-based method is used in order to calculate the uncertainty for each possible directed edge and out of this construct the Bayesian Network. Finally soft interventions are applied to the process in order to learn the real causal structure. Our approach is tested in simulations on a chemical stirred tank reactor and on an experimental laboratory plant

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Monitoring of Complex Processes with Bayesian Networks

Cited by 8 publications

References 32 publications

Uncertainty in Building Inspection and Diagnosis: A Probabilistic Model Quantification

Uncertainty in Building Inspection and Diagnosis: A Probabilistic Model Quantification

Implementation of a methodology for evaluation of the reliability of mechatronic systems

Causal structure learning in process engineering using Bayes Nets and soft interventions

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