Applications of object-oriented Bayesian networks for condition monitoring, root cause analysis and decision support on operation of complex continuous processes

Weidl, Galia; Madsen, Anders L.; Israelson, Stefan

doi:10.1016/j.compchemeng.2005.05.005

Cited by 140 publications

(77 citation statements)

References 11 publications

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“…Qualitative models for use in process fault diagnosis should capture causal relationships. As well as signed digraphs (SDG) Maurya et al, 2004;Srinivasan et al 2006), other causal representations include Multilevel Flow Modelling (Petersen, 2000;Larsson, 2007), Bayesian belief networks (Weidl, et al, 2005), rule-based systems (e.g. Blue Circle Industries, 1990) and fault tree methods as used in the alarm management of safety critical systems (e.g.…”

Section: Methods Using Qualitative Modelsmentioning

confidence: 99%

Cause-and-effect analysis in chemical processes utilizing XML, plant connectivity and quantitative process history

Thambirajah

Benabbas

Bauer

et al. 2009

Computers & Chemical Engineering

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Disturbances that spread plant-wide in a chemical process pose challenges to maintenance staff. Connections within the plant and the presence of multiple causal paths mean it is not straightforward to locate the root disturbance because the effects can propagate and be detected elsewhere. Measurementbased methods use quantitative process history to generate hypotheses about the root cause, while a separate strand of work in the literature has used causal maps and digraphs. It has been reported that both approaches can give spurious solutions, however. The idea behind this article is to reduce the number of spurious solutions by combining basic and readily-available information about the connectivity of the process with the results from causal measurement-based analysis. Connectivity information is captured from an XML description of the process schematic that complies with the CAEX schema. The capabilities of the approach and its potential for future development are discussed.

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Section: Methods Using Qualitative Modelsmentioning

confidence: 99%

Cause-and-effect analysis in chemical processes utilizing XML, plant connectivity and quantitative process history

Thambirajah

Benabbas

Bauer

et al. 2009

Computers & Chemical Engineering

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“…As an extension of conventional BN, an Object Oriented Bayesian Network (OOBN) contains, in addition to the usual nodes, instance nodes [7].The basic construction block in an OOBN is an object, which can be a physical or an abstract entity, or a relationship between two entities [8]. The object represents either a node or an instantiation of a network class (instance nodes).…”

Section: Figure1 a Simple Bn Consisting Of Three Variablesmentioning

confidence: 99%

Preliminary Study on Bridge Health Prediction Using Dynamic Objective Oriented Bayesian Network (DOOBN)

Wang

Yan

et al. 2012

Engineering Asset Management and Infrastructure Sustainability

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The availability of bridges is crucial to people's daily life and national economy. Bridge health predication plays an important role in bridge management because maintenance optimization is implemented based on prediction results of bridge deterioration. Conventional bridge deterioration models can be categorised into two groups, namely condition states models and structural reliability models. Optimal maintenance strategy should be carried out based on both condition states and structural reliability of a bridge. However, none of existing deterioration models considers both condition states and structural reliability. This study thus proposes a Dynamic Objective Oriented Bayesian Network (DOOBN) based method to overcome the limitations of the existing methods. This methodology has the ability to act upon as a flexible unifying tool, which can integrate a variety of approaches and information for better bridge deterioration prediction. Two demonstrative case studies are conducted to preliminarily justify the feasibility of the methodology.

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“…We implement the approach characterized in (Weidl et al, 2005) to deal with noisy observations. To show the influence on the performance for the sensor under test, we proceed under the assumption, that the data of all sensors of the ego vehicle is free of bias: σO = 0.…”

Section: Performance Under Noisementioning

confidence: 99%

Analysis of the Performance of a Laser Scanner for Predictive Automotive Applications

Zeisler

Maas

2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper we evaluate the use of a laser scanner for future advanced driver assistance systems. We focus on the important task of predicting the target vehicle for longitudinal ego vehicle control. Our motivation is to decrease the reaction time of existing systems during cut-in maneuvers of other traffic participants. A state-of-the-art laser scanner, the Ibeo Scala B2 R , is presented, providing its sensing characteristics and the subsequent high level object data output. We evaluate the performance of the scanner towards object tracking with the help of a GPS real time kinematics system on a test track. Two designed scenarios show phases with constant distance and velocity as well as dynamic motion of the vehicles. We provide the results for the error in position and velocity of the scanner and furthermore, review our algorithm for target vehicle prediction. Finally we show the potential of the laser scanner with the estimated error, that leads to a decrease of up to 40% in reaction time with best conditions.

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Applications of object-oriented Bayesian networks for condition monitoring, root cause analysis and decision support on operation of complex continuous processes

Cited by 140 publications

References 11 publications

Cause-and-effect analysis in chemical processes utilizing XML, plant connectivity and quantitative process history

Cause-and-effect analysis in chemical processes utilizing XML, plant connectivity and quantitative process history

Preliminary Study on Bridge Health Prediction Using Dynamic Objective Oriented Bayesian Network (DOOBN)

Analysis of the Performance of a Laser Scanner for Predictive Automotive Applications

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