TY - CHAP A1 - Weidl, Galia A1 - Vollmar, Gerhard A1 - Dahlquist, Erik T1 - Adaptive Root Cause Analysis under uncertainties in industrial process operation T2 - The Foundations of Computer Aided Process Operations (FOCAPO 2003) N2 - We discuss a Root Cause Analysis (RCA) system implementing a probabilistic approach based on Bayesian inference for adaptive reasoning under uncertainties in industrial process operation. The proposed approach is model based and accumulates the process knowledge within the problem domain, which data is gathered and stored in XML-based information server. The Bayesian networks have been created automatically from the XML-structured data. The interconnection between XML-failure trees is handled as object oriented instances of Bayesian sub-networks within master-network covering the entire process and monitoring its overall condition, output quality and equipment effectiveness. We implement sequential on-line adaptivity of models' parameters to reflect changes in process operation. The system learning can be supervised by user feedback on the actual root cause. The general RCA methodology is applied to plate cutting in a hot rolling mill. KW - uncertainties KW - Adaptive Root Cause Analysis KW - Root Cause KW - Adaptive KW - Prozesssteuerung KW - Prozessmodell Y1 - 2003 UR - https://www.researchgate.net/publication/262562640_Adaptive_Root_Cause_Analysis_under_uncertainties_in_industrial_process_operation#fullTextFileContent ER - TY - JOUR A1 - Weidl, Galia A1 - Madsen, Anders L. A1 - Dahlquist, Erik T1 - Condition Monitoring, Root Cause Analysis and Decision Support on Urgency of Actions JF - Book Series FAIA (Frontiers in Artificial Intelligence and Applications), Soft Computing Systems - Design, Management and Applications N2 - We discuss the use of a hybrid system utilizing Object Oriented Bayesian networks and influence diagrams for probabilistic reasoning under uncertainties in industrial process operations. The Bayesian networks are used for condition monitoring and root cause analysis of process operation. The recommended decision sequence of corrective actions and observations is obtained following the "myopic" approach. The BN inference on most probable root cause is used in an influence diagram for taking decisions on urgency of corrective actions vs. delivery deadline. The build-in chain of causality from root cause to process faults can provide the user with explanation facility and a simulation tool of the effect of intended actions. KW - OOBN KW - Bayesian Networks KW - Prozesssteuerung KW - Prozessanalyse Y1 - 2002 UR - https://www.researchgate.net/publication/228719447_Condition_Monitoring_Root_Cause_Analysis_and_Decision_Support_on_Urgency_of_Actions#fullTextFileContent VL - 2002 IS - 87 SP - 221 EP - 230 ER - TY - JOUR A1 - Dahlquist, Erik A1 - Lindberg, Thomas A1 - Karlsson, Christer Per A1 - Weidl, Galia A1 - Bigaran, Carlo A1 - Davey, Austin T1 - Integrated Process Control, Fault Diagnostics, Process Optimization and Production Planning - Industrial IT JF - IFAC Proceedings Volumes N2 - In the presentation a total system is presented, making use of data reconciliation, different types of diagnostics with respect to sensors, control loops and processes. These are used as inputs to a root cause analysis system, optimization and advanced control, using among others MPC, model predictive control. The system is being implemented at Visy Pulp and Paper mill in Tumut, Australia. KW - MPC KW - Model Predictive Control KW - root cause analysis KW - RCA KW - Prozesssteuerung KW - Papierindustrie Y1 - 2001 UR - https://www.sciencedirect.com/science/article/pii/S147466701733567X?via%3Dihub U6 - https://doi.org/10.1016/S1474-6670(17)33567-X VL - 2001 IS - 34/27 SP - 47 EP - 55 ER - TY - CHAP A1 - Weidl, Galia A1 - Dahlquist, Erik T1 - ROOT CAUSE ANALYSIS FOR PULP AND PAPER APPLICATIONS T2 - Proceedings of the 10th Control Systems Conference, Stockholm, Sweden, June 3-5, 2002 N2 - We propose a methodology for Root Cause Analysis (RCA), allowing fast and flexible decision support for operators, maintenance staff and process engineers in pulp and paper industry. RCA can identify non-obvious process problems and is therefore a powerful complement to normal automatic control. The general methodology is applied to a continuous digester. KW - RCA KW - Root Cause Analysis KW - Prozessanalyse KW - Papierindustrie Y1 - 2002 UR - https://www.researchgate.net/publication/262562479_ROOT_CAUSE_ANALYSIS_FOR_PULP_AND_PAPER_APPLICATIONS#fullTextFileContent SP - 343 EP - 347 ER - TY - CHAP A1 - Weidl, Galia T1 - Freiraumbewertung für Spurwechselmanöver mit Bayes-Netzen T2 - 7. VDI/VDE Fachtagung AUTOREG Auf dem Weg zum automatisierten Fahren N2 - Kurzfassung Diese Arbeit stellt ein robustes wissensbasiertes Verfahren zur Lückenbewertung für Spur-wechselmanöver vor. Zur Modellierung wurden dynamische Bayes-Netzwerke eingesetzt und mit Hilfe von Lernalgorithmen die Erkennungsleistung verbessert. Die Testergebnisse zeigen eine sehr hohe Trefferquote. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem Y1 - 2015 UR - https://www.researchgate.net/publication/279886700_Freiraumbewertung_fur_Spurwechselmanover_mit_Bayes-Netzen#fullTextFileContent ER - TY - CHAP A1 - Chaar, Mohamad Mofeed A1 - Weidl, Galia A1 - Raiyn, Jamal T1 - Analyse the effect of fog on the perception T2 - Conference: International Symposium on Transportation Data & Modelling (ISTDM 2023) KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Autonomes Fahrzeug KW - Fahrerassistenzsystem KW - Nebel Y1 - 2023 UR - https://www.researchgate.net/publication/369484982_Analyse_the_effect_of_fog_on_the_perception#fullTextFileContent ER - TY - CHAP A1 - Valencia, Yeimy A1 - Normann, Marc A1 - Sapsai, Iryna A1 - Abke, Jörg A1 - Madsen, Anders L. A1 - Weidl, Galia T1 - Learning Style Classification by Using Bayesian Networks Based on the Index of Learning Style T2 - ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education, June 2023 KW - Lernstil KW - Fragebogen Y1 - 2023 U6 - https://doi.org/10.1145/3593663.3593685 SP - 73 EP - 82 ER - TY - CHAP A1 - Jarosch, Oliver A1 - Naujoks, Frederik A1 - Wandtner, Bernhard A1 - Gold, Christian A1 - Marberger, Claus A1 - Weidl, Galia A1 - Schrauf, Michael T1 - The Impact of Non-Driving Related Tasks on Take-over Performance in Conditionally Automated Driving – A Review of the Empirical Evidence T2 - 9. Tagung Automatisiertes Fahren, München, Partner TÜV Süd, November 2019 N2 - Conditional automated driving (CAD) systems (SAE level 3) will soon be introduced to the public market. This automation level is designed to take care of all aspects of the dynamic driving task in specific application areas and does not require the driver to continuously monitor the system performance. However, in contrast to higher levels of automation the "fallback-ready" user always has to be able to regain control if requested by the system. As CAD allows the driver to engage in non-driving-related tasks (NDRTs) past human factors research has looked at their effects on takeover time and quality especially in short-term takeover situations. In order to understand how takeover performance is impacted by different NDRTs, this paper summarizes and compares available results according to the NDRT's impact on the sensoric, motoric and cognitive transition. In addition, aspects of arousal and motivation are considered. Due to the heterogeneity of the empirical work and the available data practically relevant effects can only be attested for NDRTs that cause severe discrepancies between the current driver state and the requirements of the takeover task, such as sensoric and motoric unavailability. The paper concludes by discussing methodological issues and recommending the development of standardized methods for the future. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem KW - Autonomes Fahrzeug Y1 - 2019 UR - https://www.researchgate.net/publication/338644809_The_Impact_of_Non-Driving_Related_Tasks_on_Take-over_Performance_in_Conditionally_Automated_Driving_-_A_Review_of_the_Empirical_Evidence#fullTextFileContent ER - TY - CHAP A1 - Weidl, Galia A1 - Singhal, Virat A1 - Petrich, Dominik A1 - Kaspar, Dietmar A1 - Wedel, Andreas A1 - Breuel, Gabi T1 - Collision Risk Prediction and Warning at Road Intersections Using an Object Oriented Bayesian Network T2 - 5th International Conference Automotive User Interfaces and Interactive Vehicular Applications (Automotive UI,13), Oct.28-30, 2013 N2 - This paper describes a novel approach to situation analysis at intersections using object-oriented Bayesian networks. The Bayesian network infers the collision probability for all vehicles approaching the intersection, while taking into account traffic rules, the digital street map, and the sensors' uncertainties. The environment perception is fused from communicated data, vehicles local perception and self-localization. Thus, a cooperatively validated set of data is obtained to characterize all objects involved in a situation (resolving occlusions). The system is tested with data, acquired by vehicles with heterogenic equipment (without/with perception). In a first step the probabilistic mapping of a vehicle onto a fixed set of traffic lanes and forward motion predictions is introduced. Second, criticality measures are evaluated for these motion predictions to infer the collision probability. In our test vehicle this probability is then used to warn the driver of a possible hazardous situation. It serves as a likelihood alarm parameter for deciding the intensity of HMI acoustic signals to direct the driver's attention. First results in various simulated and live real-time scenarios show, that a collision can be predicted up to two seconds before a possible impact by applying the developed Bayesian network. The extension of this network to further situation features is the content of ongoing research. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem KW - Künstliche Intelligenz Y1 - 2013 UR - https://www.researchgate.net/publication/267764514_Collision_Risk_Prediction_and_Warning_at_Road_Intersections_Using_an_Object_Oriented_Bayesian_Network#fullTextFileContent U6 - https://doi.org/10.1145/2516540.2516577 ER - TY - JOUR A1 - Kaspar, Dietmar A1 - Weidl, Galia A1 - Dang, Thao A1 - Breuel, Gabi A1 - Tamke, Andreas A1 - Rosenstiel, Wolfgang T1 - Object-Oriented Bayesian Networks for Detection of Lane Change Maneuvers JF - IEEE Intelligent Transportation Systems Magazine N2 - In this paper we introduce a novel approach towards the recognition of typical driving maneuvers in structured highway scenarios and identify some of the key benefits of traffic scene modeling with object-oriented Bayesian networks (OOBNs). The approach exploits the advantages of an introduced lane-related coordinate system together with individual occupancy grids for all vehicles. This combination allows for an efficient classification of the existing vehiclelane and vehicle-vehicle relations in a traffic scene and thus substantially improves the understanding of complex traffic scenes. We systematically propagate probabilities and variances within our network which results in probabilistic sets of the modeled driving maneuvers. Using this generic approach, we are able to classify a total of 27 driving maneuvers including merging and object following. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem Y1 - 2011 UR - https://www.researchgate.net/publication/249023568_Object-Oriented_Bayesian_Networks_for_Detection_of_Lane_Change_Maneuvers#fullTextFileContent U6 - https://doi.org/10.1109/IVS.2011.5940468 IS - 4(3) SP - 19 EP - 31 ER - TY - CHAP A1 - Raiyn, Jamal A1 - Weidl, Galia T1 - Naturalistic Driving Studies Data Analysis Based on a Convolutional Neural Network T2 - VEHITS 2023: 9th International Conference on Vehicle Technology and Intelligent Transport Systems N2 - The new generation of autonomous vehicles (AVs) are being designed to act autonomously and collect travel data based on various smart devices and sensors. The goal is to enable AVs to operate under their own power. Naturalistic driving studies (NDSs) collect data continuously from real traffic activities, in order not to miss any safety-critical event. In NDSs of AVs, however, the data they collect is influenced by various sources that degrade their forecasting accuracy. A convolutional neural network (CNN) is proposed to process a large amount of traffic data in different formats. A CNN can detect anomalies in traffic data that negatively affect traffic efficiency and identify the source of data anomalies, which can help reduce traffic congestion and vehicular queuing. KW - Autonomes Fahrzeug KW - Künstliche Intelligenz Y1 - 2023 UR - https://www.researchgate.net/publication/368332673_Naturalistic_Driving_Studies_Data_Analysis_Based_on_a_Convolutional_Neural_Network#fullTextFileContent U6 - https://doi.org/10.5220/0011839600003479 ER - TY - CHAP A1 - Weidl, Galia A1 - Breuel, Gabi T1 - Overall Probabilistic Framework for Modeling and Analysis of Intersection Situations T2 - Advanced Microsystems for Automotive Applications 2012 N2 - We propose a system design for preventive traffic safety in general intersection situations involving all present traffic participants (vehicles and vulnerable road users) in the context of their environment and traffic rules. It exploits the developed overall probabilistic framework for modeling and analysis of intersection situations under uncertainties in the scene, in measured data or in communicated information. It proposes OOBN modeling for the cognitive assessment of potential and real danger in intersection situations and presents schematically an algorithm for multistage cognitive situation assessment. A concept for the interaction between situation assessment and the proposed Proactive coaching Safety Assistance System (PaSAS) is outlined. The assessment of danger in a situation development serves as a filter for the output and intensity of HMI-signals for directing driver's attention to essentials. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem Y1 - 2012 UR - https://link.springer.com/chapter/10.1007/978-3-642-29673-4_24 SN - 978-3-642-29673-4 U6 - https://doi.org/10.1007/978-3-642-29673-4_24 SP - 257 EP - 268 PB - Springer ER - TY - CHAP A1 - Kaspar, Dietmar A1 - Weidl, Galia A1 - Dang, Thao A1 - Breuel, Gabi A1 - Tamke, Andreas A1 - Rosenstiel, Wolfgang T1 - Erkennung von Fahrmanövern mit objektorientierten Bayes-Netzen T2 - 7. Workshop. Fahrerassistenzsysteme. FAS2011 N2 - In diesem Artikel wird ein Ansatz zur Erkennung von Spurwechselmanövern mit Hilfe von objekt-orientierten Bayes Netzen beschrieben. Dieser Ansatz ist eine Erweiterung von Grundlagenarbeiten zur Einscherererkennung. Zunächst werden die zur Erkennung von Spurwechselvorgängen erforderlichen Fahrsituationsmerkmale vorgestellt. Darauf aufbauend wird das entwickelte objekt-orientierte Bayes Netz zur Modellierung der Spurwechsel erläutert. Dabei wird ein Spurwechsel als eine Beziehung zwischen zwei Fahrzeugen betrachtet. Dabei kann ein Fahrzeug in der Spur bleiben, die Spur nach links oder nach rechts wechseln. Durch die Kombination der Möglichkeiten der beiden Fahrzeuge entstehen 9 Klassen. Aus den relativen Positionen der Fahrzeuge ergeben sich aus den 9 Situationsklassen 27 mögliche Spurwechselmanöver. Dabei ist das Einschervorgang ein Sonderfall der modellierten Fahrmanöver. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem Y1 - 2011 UR - https://www.researchgate.net/publication/249226427_Erkennung_von_Fahrmanovern_mit_objektorientierten_Bayes-Netzen#fullTextFileContent ER - TY - CHAP A1 - Weidl, Galia A1 - Madsen, Anders L. A1 - Dahlquist, Erik T1 - Object Oriented Bayesian Networks for Industrial Process Operation T2 - Proceedings of the first Bayesian Application Modeling Workshop at the 19th Conference in Uncertainty in Artificial Intelligence, 2003 N2 - We present an application, where extensions of existing methods for decision-theoretic troubleshooting are used for industrial process operation and asset management. The extension includes expected average cost of asset management actions, prediction of signals' level-trend development, risk assessment for disturbance analysis and predictive maintenance on demand. KW - Bayesian Networks KW - OOBN KW - Prozesssteuerung Y1 - 2003 UR - https://www.researchgate.net/publication/2942492_Object_Oriented_Bayesian_Networks_for_Industrial_Process_Operation#fullTextFileContent ER - TY - JOUR A1 - Weidl, Galia A1 - Madsen, Anders L. A1 - Israelson, S. T1 - Applications of object-oriented Bayesian networks for condition monitoring, root cause analysis and decision support on operation of complex continuous processes JF - Computers & Chemical Engineering N2 - The increasing complexity of large-scale industrial processes and the struggle for cost reduction and higher profitability means automated systems for processes diagnosis in plant operation and maintenance are required. We have developed a methodology to address this issue and have designed a prototype system on which this methodology has been applied. The methodology integrates decision-theoretic troubleshooting with risk assessment for industrial process control. It is applied to a pulp digesting and screening process. The process is modeled using generic object-oriented Bayesian networks (OOBNs). The system performs reasoning under uncertainty and presents to users corrective actions, with explanations of the root causes. The system records users’ actions with associated cases and the BN models are prepared to perform sequential learning to increase its performance in diagnostics and advice. KW - Bayes Networks KW - Prozessüberwachung KW - Fehlererkennung KW - Prozesssteuerung Y1 - 2005 UR - https://www.sciencedirect.com/science/article/abs/pii/S009813540500133X?via%3Dihub U6 - https://doi.org/10.1016/j.compchemeng.2005.05.005 VL - 2005 IS - 29/9 SP - 1996 EP - 2009 ER - TY - JOUR A1 - Weidl, Galia A1 - Rode, Manfred A1 - Horch, Alexander A1 - Shaw, Christopher A1 - Vollmer, Andreas T1 - Automated root cause analysis of faults and disturbances in rolling mills JF - Stahl und Eisen N2 - The developed methodology for Root Cause Analysis (RCA) demonstrates a decision support tool evaluating the process state based on both qualitative and quantitative information. The presented RCA system uses the available data to extract the most probable root causes and proposes an action sequence. The learning ability of the system allows its sequential on-line adaptation to reflect changes in process operation. N2 - Die entwickelte Methodik für die Fehlerursachenanalyse (RCA) stellt ein Instrument zur Entscheidungsunterstützung dar, das den Prozesszustand auf der Grundlage qualitativer und quantitativer Informationen bewertet. Das vorgestellte RCA-System nutzt die verfügbaren Daten, um die wahrscheinlichsten Grundursachen zu ermitteln und schlägt eine Handlungssequenz vor. Die Lernfähigkeit des Systems ermöglicht seine sequentielle Online-Anpassung, um Änderungen im Prozessbetrieb zu berücksichtigen. KW - rolling mills KW - Walzwerke KW - Walzwerk KW - Prozessanalyse Y1 - 2005 UR - https://www.researchgate.net/publication/296803110_Automated_root_cause_analysis_of_faults_and_disturbances_in_rolling_mills VL - 2005 IS - 125/8 SP - 29 EP - 34 ER - TY - CHAP A1 - Weidl, Galia A1 - Madsen, Anders L. A1 - Dahlquist, Erik T1 - Applications of object-oriented Bayesian networks for causal analysis of process disturbances T2 - SIMS'2003 (44th International Conference of the Scandinavian Simulation Society) N2 - We discuss a hybrid approach for causal analysis of disturbances in industrial process operation. It represents a combination of OOBN with first level diagnostic packages and physical models serving as agents in the system design and providing evidence for automated reasoning on abnormality in process operation. The aim is causal analysis of non-measurable disturbances as a decision advice complement to the distributed control system (DCS). The approach includes prediction of signals' level-trend development, risk assessment for disturbance analysis and predictive maintenance on demand. The methodology has been applied on a screening process with a pressure-flow network in a Pulp Mil. KW - OOBN KW - DCS KW - Bayesian Networks KW - Prozessmodell KW - Prozesssteuerung Y1 - 2003 UR - https://www.researchgate.net/publication/228679172_Applications_of_object-oriented_Bayesian_networks_for_causal_analysis_of_process_disturbances#fullTextFileContent ER - TY - CHAP A1 - Weidl, Galia T1 - ADAPTIVE RISK ASSESSMENT IN COMPLEX LARGE SCALE PROCESSES WITH REDUCED COMPUTATIONAL COMPLEXITY T2 - 9th International Conference on Industrial Engineering Theory, Applications & Practice, November 27-30, 2004 N2 - We have developed a methodology that targets risk assessment for process operation. It includes both abnormality prediction and evaluation of its development, provided no corrective actions are taken, as well as a possibility to examine the impact of intended actions. It handles the uncertainties in the domain and the big number of influences on the effect variables by utilizing causal probabilistic modeling. The process is modeled by Hidden Markov Models (HMM), and object oriented dynamic Bayesian networks (OOBNs). Various modeling techniques and assumptions have been used to reduce the computational complexity and ensure fast inference. The methodology is applied in a case study. KW - Adaptive Risk Management KW - Hidden Markov Models KW - HMM KW - OOBN KW - Bayesian Networks KW - Prozessmodell Y1 - 2004 UR - http://www.orsnz.org.nz/conf39/ProgramSummary.pdf ER - TY - CHAP A1 - Weidl, Galia A1 - Madsen, Anders L. A1 - Dahlquist, Erik T1 - Decision Support on Complex Industrial Process Operation T2 - Bayesian Networks: A Practical Guide to Applications N2 - Introduction: A methodology for Root Cause AnalysisPulp and paper applicationThe ABB Industrial IT platformConclusion KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Künstliche Intelligenz KW - Papierindustrie Y1 - 2008 UR - https://www.wiley.com/en-gb/Bayesian+Networks%3A+A+Practical+Guide+to+Applications-p-9780470060308 U6 - https://doi.org/10.1002/9780470994559.ch18 SP - 313 EP - 328 PB - Wiley ER - TY - JOUR A1 - Naujoks, Frederik A1 - Grattenthaler, Heidi A1 - Neukum, Alexandra A1 - Weidl, Galia A1 - Petrich, Dominik T1 - Effectiveness of advisory warnings based on cooperative perception JF - IET Intelligent Transport Systems N2 - Cooperative perception makes it possible – in addition to emergency warnings – to provide drivers with early advisory warnings about potentially dangerous driving situations. Based on research results pertaining to imminent crash warnings, it was expected that the effectiveness of such advisory warnings depends on situation-specific anticipations by the driver. During a simulator study, N = 20 drivers went through a wide range of longitudinal traffic and intersection scenarios. The scenarios varied in the possibility to anticipate traffic conflicts (Anticipation: high vs. low) and were completed under different visibility conditions (Visibility: obstructed vs. visible), with and without driver assistance based on cooperative perception (i.e., visual-auditory advisory warnings two seconds prior to the last-possible warning moment; assistance: no assistance vs. with assistance). The warning concept was based on empirical pre-studies and previously validated on a public test intersection. During non-assisted driving, critical situations were mainly experienced when the possibility to anticipate traffic conflicts was low. Visual obstructions lead to a further increase in the frequency of critical situations. Furthermore, the results indicate a clear mitigation of critical encounters when providing early advisory warnings which is independent from sight obstructions. This applies particularly to surprising and unexpected scenarios and thus illustrates the potential of cooperative perception to enhance active traffic safety. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem Y1 - 2015 UR - https://www.researchgate.net/publication/276248479_Effectiveness_of_advisory_warnings_based_on_cooperative_perception#fullTextFileContent U6 - https://doi.org/10.1049/iet-its.2014.0190 VL - 2015 IS - 9 SP - 606 EP - 617 ER -