@inproceedings{1994, abstract = {{In the filling and packaging industry, the trend is towards self-diagnosis, optimization, and quality monitoring of processes. The aim is to increase production volumes and the quality. These concepts require continuous monitoring and anomaly detection of the filling process. In addition, a root cause analysis of the failure is required because not every failure can be simulated or measured previously. Standard anomaly detection methods have no integrated root cause analysis. In this paper a fusion system is utilises for the detection of different unknown anomalies and also the failure source of them. The performance of this method is benchmarked with a real-word filling process.}}, author = {{Bator, Martyna and Dicks, Alexander and Deppe, Sahar and Lohweg, Volker}}, booktitle = {{24nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA2019) }}, isbn = {{978-1-7281-0304-4}}, issn = {{1946-0759}}, location = {{ Zaragoza, Spain }}, publisher = {{IEEE}}, title = {{{Anomaly Detection with Root Cause Analysis for Bottling Process}}}, doi = {{10.1109/ETFA.2019.8869514}}, year = {{2019}}, } @inproceedings{2001, author = {{Dicks, Alexander and Wissel, Christian and Bator, Martyna and Lohweg, Volker}}, booktitle = {{Kommunikation und Bildverarbeitung in der Automation - Ausgewählte Beiträge der Jahreskolloquien KommA und BVAu 2018}}, location = {{Lemgo}}, pages = {{331--345}}, publisher = {{Springer}}, title = {{{Bildverarbeitung im industriellen Umfeld von Abfüllanlagen}}}, doi = {{10.1007/978-3-662-59895-5_24}}, year = {{2019}}, } @inproceedings{2006, abstract = {{We present an approach for feature extraction in the context of condition monitoring of a bottling process. A special focus lies on the characterisation and evaluation of liquid textures. The approach will feed into a sensor and information fusion system to monitor a bottling process. Requirements like real-time capabilities, data reduction and resource limitations necessitate a fusion approach which capture physical effects of different sensors, extract appropriate features and combine them into one state for the complete filling process. Special attention is paid to the feature extraction of the visual sensor signals to monitor the filling level, the amount of foam and the degree of turbulence in the liquid.}}, author = {{Bator, Martyna and Wissel, Christian and Dicks, Alexander and Lohweg, Volker}}, booktitle = {{23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}}, location = {{Torino, Italy}}, title = {{{Feature Extraction for a Conditioning Monitoring System in a Bottling Process.}}}, doi = {{ 10.1109/ETFA.2018.8502472}}, year = {{2018}}, } @inproceedings{2033, abstract = {{Cash machines or automated teller machines (ATMs) are one of the typical ways to get cash around the world. Such machines are under a variety of criminal attacks. Most of the manipulations are performed through skimming. In 2014, such attacks led to a damage of approx. 280 million Euro within the EU. In this paper, we propose an approach to detect anomalies and attacks on ATMs via motif discovery. Motifs are frequently unknown occurring sequences or events in a time series signal. State of the ATM is captured by innovative piezoelectric sensor networks to analyse the occurring vibrations. The captured signals are inspected by the Complex Quad-Tree Wavelet Packet transform which provides broad frequency analysis of a signal in various scales. Next, features are extracted from the selected scale based on the information content, to detect motifs. Detected motifs provide the prototype patterns for anomaly detection or classification tasks.}}, author = {{Deppe, Sahar and Dicks, Alexander and Lohweg, Volker}}, booktitle = {{21th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2016), Berlin, }}, title = {{{Anomaly Detection on ATMs via Time Series Motif Discovery}}}, year = {{2016}}, } @inproceedings{2133, abstract = {{Due to the material changes of components from metal to plastic or composite materials, the structural health monitoring finds more and more interest in the industrial fields. The reason is that these materials are more vulnerable to damage or impacts which cannot be optically detected. In this contribution we present a method to analyze the structure of plastic components with piezo-electrical sensors and actuators. The components are stimulated by actuators, and sensors capture the injected vibrations. These signals are decomposed into Intrinsic Mode Functions to compute statistical features. A Fuzzy-Pattern-Classifier is applied to detect structural modifications at the components under test.}}, author = {{Dicks, Alexander and Lohweg, Volker and Wittke, Henrik and Linke, Stefan}}, booktitle = {{20th IEEE International Conference on Emerging Technologies and Factory Automation}}, keywords = {{Sensors, Actuators, Finite element analysis, Plastics, Modal analysis, Monitoring, Empirical mode decomposition}}, title = {{{Structural Health Monitoring of Plastic Components with Piezoelectric Sensors}}}, doi = {{ 10.1109/ETFA.2015.7301595}}, year = {{2015}}, } @inproceedings{2164, author = {{Neumann, Richard and Dicks, Alexander and Mönks, Uwe and Lohweg, Volker}}, booktitle = {{24. Workshop Computational Intelligence}}, isbn = {{978-3-7315-0275-3}}, pages = {{315--332}}, publisher = {{VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA)}}, title = {{{Fuzzy Pattern Klassifikation von Datensätzen mit nichtkonvexen Objektmorphologien}}}, year = {{2014}}, } @article{2135, abstract = {{Eine Zustandsüberwachung elektrischer Antriebe erfolgt derzeit in der Regel durch Einsatz spezieller Sensorik, bspw. durch Vibrationsmessungen. Außerdem werden die Antriebe lediglich isoliert betrachtet, eine Zusammenführung anfallender Informationen eines räumlich verteilten Antriebsverbunds findet meist nicht statt. Es wird ein neuartiges Motor-as-Sensor-Konzept vorgeschlagen und validiert, das eine antizipatorische Zustandsüberwachung ohne Einsatz zusätzlicher Sensorik allein durch Verarbeitung der phasenbezogenen Motorströme ermöglicht. Zusätzlich wird ein Informationsfusionskonzept vorgestellt, das die Informationen aller im Verbund beteiligten Antriebe zusammenführt, um darüber eine mit weniger Unsicherheiten behaftete Aussage über den Zustand einer Applikation herbeizuführen. Das Hauptaugenmerk liegt hierbei insbesondere auf der Beherrschung der anfallenden riesigen Datenmengen. die zur Verarbeitung in eingebetteten Systemen reduziert werden müssen.}}, author = {{Mönks, Uwe and Bator, Martyna and Dicks, Alexander and Lohweg, Volker}}, isbn = {{978-3-942647-29-8}}, journal = {{Wissenschaftsforum Intelligente Technische Systeme (Heinz Nixdorf Institut, Paderborn)}}, pages = {{305--315}}, publisher = {{Universität Paderborn}}, title = {{{Informationsfusion mit verteilter elektromotorischer Sensorik im Maschinen- und Anlagenbau}}}, volume = {{9. Paderborner Workshop Entwurf mechatronischer Systeme}}, year = {{2013}}, } @inproceedings{2137, abstract = {{Systems for process automation become increasingly complex and also tend to be composed of autonomous subsystems, which is strongly driven by the progress made in information technology. An active field of research is the implementation of monitoring and control at sub-system level using cognitive approaches. In this paper we present a method for autonomous and sensorless condition monitoring of an electric drive train. Based on experiment design we measured phase currents of a physical demonstrator device including mechanical defects and extracted signal features using proper orthogonal decomposition. In favor of classification of different defect states we performed a linear discriminant analysis, which yields appropriate data for a Fuzzy-Pattern-Classification algorithm. As a result we were able to identify different reference defect states as well as previously unknown states.}}, author = {{Bayer, Christian and Bator, Martyna and Enge-Rosenblatt, Olaf and Mönks, Uwe and Dicks, Alexander and Lohweg, Volker}}, isbn = {{978-1-4799-0862-2}}, publisher = {{18th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}}, title = {{{Sensorless Drive Diagnosis Using Automated Feature Extraction, Significance Ranking and Reduction.}}}, doi = {{ 10.1109/ETFA.2013.6648126}}, year = {{2013}}, } @inproceedings{2142, abstract = {{Die aktive Zustandsüberwachung von Automatisierungssystemen rückt immer weiter in den Vordergrund und ist daher ein zentraler Forschungsgegenstand. In diesem Beitrag werden Ansätze der sensorlosen Überwachung eines Synchronmotors diskutiert. Basierend auf Messungen der Phasenströme des Motors werden mit der Hilbert-Transformation bzw. mit der Empirical Mode Decomposition charakteristische Merkmale aus den Signalen berechnet. Anschließend werden diese mittels Hauptkomponentenanalyse bzw. der linearen Diskriminanzanalyse reduziert. Die daraus berechneten Charakteristischen Merkmale dienen als Grundlage für die abschließende Fuzzy-Pattern-Klassifikation. Basierend auf dem erläuterten Ansatz ist die Identifikation typischer Betriebs- bzw. Fehlerzustände, aber auch das Erkennen nicht gelernter Zustände möglich. Das dabei vorgestellte Vorgehen ist vergleichsweise generisch und lässt sich gut auf andere Anwendungsgebiete übertragen.}}, author = {{Paschke, Fabian and Bayer, Christian and Bator, Martyna and Mönks, Uwe and Dicks, Alexander and Enge-Rosenblatt, Olaf and Lohweg, Volker}}, booktitle = {{23. Workshop Computational Intelligence 2013. Proceedings}}, editor = {{Hoffmann, F.}}, isbn = {{978-3-7315-0126-8}}, location = {{Dortmund}}, pages = {{211--225}}, publisher = {{KIT Scientific Publishing}}, title = {{{Sensorlose Zustandsüberwachung an Synchronmotoren.}}}, volume = {{46}}, year = {{2013}}, } @inproceedings{2105, author = {{Dicks, Alexander and Bator, Martyna and Lohweg, Volker and Faltinski, Sebastian and Niggemann, Oliver}}, booktitle = {{Cyber-Physical Systems – Enabling Multi-Nature Systems (CPMNS), Dresden, April 18, }}, isbn = {{978-3-8396-0398-7 }}, pages = {{51--56}}, publisher = {{Fraunhofer-Verlag}}, title = {{{Cyber-Physical Systems im Maschinen- und Anlagenbau – ein Konzept für die Zukunft?}}}, year = {{2012}}, } @inproceedings{2119, abstract = {{In this paper, it is proposed a feature selection procedure based on Linear Discriminant Analysis. The aim behind this approach is to obtain a minimal set of features still enabling a separation between a number of different classes. Additionally, the reduced number of features implies faster computation and enables resource-limited hardware implementations for real-time signal processing applications. Also, incorporating only a small number of features retains the application's interpretability as a feature space of maximum three features can be visualised directly. Due to this, an expert can directly follow a decision system's answer. The proposed method has been evaluated in the context of an electric drive diagnosis application. In this scope, the LDA feature selection approach is at least as good as the benchmarked feature selection methods. When regarding only a minimal number of features, LDA outperforms the other approaches in terms of classification accuracy. As a secondary result. one can see how important a sensible choice of features is. While some arbitrary combinations produce completely inseparable feature spaces. Three are still combinations that can separate the classes even linearly such that no sophisticated classification concept (e.g. SVM) is needed. The authors are aware of the fact that the findings are shown only in the context of one specific application. Based on the work elaborated here, further research towards generalisation of the proposed approach is intended to be carried out. Additionally, the findings shall be examined using classifier concepts different from SVM, such as Fuzzy Pattern Classifiers.}}, author = {{Bator, Martyna and Dicks, Alexander and Mönks, Uwe and Lohweg, Volker}}, booktitle = {{22. Workshop Computational Intelligence, 06.-7.12.2012, Dortmund}}, editor = {{Frank Hoffmann, Eike Hüllermeier}}, isbn = {{978-3-86644-917-6}}, pages = {{163--177}}, publisher = {{VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA)}}, title = {{{Feature Extraction and Reduction Applied to Sensorless Drive Diagnosis}}}, year = {{2012}}, } @inproceedings{2102, abstract = {{Die Zustandsüberwachung erfolgt derzeit in der Regel durch Einsatz spezieller Sensorik, bspw. durch Vibrationsmessungen. Außerdem werden die Antriebe lediglich isoliert betrachtet, eine Zusammenführung anfallender Informationen eines räumlich verteilten Antriebsverbunds findet meist nicht statt. Im Folgenden wird ein neuartiges Motor-as-Sensor-Konzept vorgeschlagen und validiert, das eine antizipatorische Zustandsüberwachung ohne Einsatz zusätzlicher Sensorik allein durch Verarbeitung der phasenbezogenen Motorströme ermöglicht. Zusätzlich wird ein Informationsfusionskonzept vorge-stellt, das die Informationen aller im Verbund beteiligten Antriebe zusammenführt, um darüber eine mit weniger Unsicherheiten behaftete Aussage über den Zustand einer Ap-plikation herbeizuführen. Das Hauptaugenmerk liegt hierbei insbesondere auf der Beherrschung der anfallenden riesigen Datenmengen, die zur Verarbeitung in eingebetteten Systemen reduziert werden müssen.}}, author = {{Voth, Karl and Dicks, Alexander and Lohweg, Volker}}, keywords = {{Sensor- und Informationsfusion, elektrischer Antrieb, Cyber-Physical System, Industrie 4.0, Big Data}}, title = {{{Konfliktlösende Informationsfusion zur Maschinendiagnose am Beispiel von Extrusionsanlagen, 21. Workshop Computational Intelligence, VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA), 30. November - 02. Dezember 2011, Dortmund}}}, year = {{2011}}, } @inproceedings{2079, author = {{Iqbal, Taswar and Dicks, Alexander and Lohweg, Volker}}, publisher = {{32nd Annual Conference on Artificial Intelligence Paderborn | September 15 – 18, 2009, accepted for Publication}}, title = {{{Metal Surface Coding as a trusted body for brand labeling, KI 2009, Artificial Intelligence and Automation (AI at Universities of Applied Sciences)}}}, year = {{2009}}, }