Pursuing the PhD in Industry 4.0-focused Fault Diagnosis. Working on my project, papers, chapters and as reviewer.
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My research interests are machine learning, artificial intelligence, time-series analytics, and outlier detection in smart factory/industry 4.0 environments. The primary ethical purpose of my research is to assist the human in the chain and help to persist in a changing industry. A professional is a valuable source of expertise and should be supported to be able to adapt to the new processes under Industry 4.0. Reviewer for: Big Data Research Journal (Elsevier) and IV2022 (IEEE)
April 2016 - present
- Research Associate
- Work on different research projects, such as ProDok 4.0 (Smart Documentation for the Smart Factory) or VisCost (COST-Action Preparation) as Researcher and Software-Engineer.
October 2015 - May 2017
- Software Engineer
- Successfully, realized small to mid-size projects with a small team of highly skilled programmers, from beginning till the end. Employed as Project-, Team-Lead and as Software-Engineer.
The integration of cyber-physical systems accelerates Industry 4.0. Smart factories become more and more complex, with novel connections, relationships, and dependencies. Consequently, complexity also rises with the vast amount of data. While acquiring data from all the involved systems and protocols remains challenging, the assessment and reasonin...
The increase of productivity and decrease of production loss is an important goal for modern industry to stay economically competitive. For that, efficient fault management and quick amendment of faults in production lines are needed. The prioritization of faults accelerates the fault amendment process but depends on preceding fault detection and c...
Machine intelligence, a.k.a. artificial intelligence (AI) is one of the most prominent and relevant technologies today. It is in everyday use in the form of AI applications and has a strong impact on society. This article presents selected results of the 2020 Dagstuhl workshop on applied machine intelligence. Selected AI applications in various dom...
Die visuelle Projektion von heterogenen (z.B. Forschungs-)Daten auf einer 2-dimensionalen Fläche, wie etwa einem Bildschirm, wird als Datenvisualisierung bezeichnet. Datenvisualisierung ist ein Oberbegriff für verschiedene Arten der visuellen Projektion. In diesem Kapitel wird zunächst der Begriff definiert und abgegrenzt. Der Fokus des Kapitels li...
Cyber-physical systems in smart factories get more and more integrated and interconnected. Industry 4.0 accelerates this trend even further. Through the broad interconnectivity a new class of faults arise, the contextual faults, where contextual knowledge is needed to find the underlying reason. Fully-automated systems and the production line in a...
Analytical Reasoning by applying machine learning approaches, artificial intelligence, NLP and visualizations allow to get deep insights into the different domains of various stakeholders and enable to solve complex tasks. Thereby the tasks are very heterogenous and subject of investigation in the different areas of application. These tasks or chal...
Scientific publications are an essential resource for detecting emerging trends and innovations in a very early stage, by far earlier than patents may allow. Thereby Visual Analytics systems enable a deep analysis by applying commonly unsupervised machine learning methods and investigating a mass amount of data. A main question from the Visual Anal...
Mobility, transportation and logistics are more and more influenced by a variety of indicators such as new technological developments, ecological and economic changes, political decisions and in particular humans’ mobility behavior. These indicators will lead to massive changes in our daily live with regards to mobility, transportation and logistic...
Industry is changing rapidly under industry 4.0. The manufacturing process and its cyber-physical systems (CPSs) produce large amounts of data with many relationships and dependencies in the data. Outlier detection and problem solving is difficult in such an environment. We present an unsupervised outlier detection method to find outliers in tempor...
When machine errors occur in factories, it is important to act quickly in an appropriate way. Depending on the complexity of the error situation and the skill level of the personnel, it is important to identify the appropriate technical documentation quickly. This paper presents a methodology for semantically matching symptoms and causes in error s...
The main goal of this project is to create a sustainable European strategic alliance to foster European research and innovation in the area of Visual Analytics, Artificial Intelligence, Simulation, Prediction and Planning of emerging technologies and innovations for business and eGovernance. The strategic alliance is set up for a long period and serves the purpose to discover continuously new opportunities to strengthen the European activities of the Darmstadt University of Applied Sciences. It aims at investigating various European funding and networking opportunities and submitting proposals to the various European research‐related programs. This project targets networking activities to help setting up the strategic alliance. More information on: http://s.vis.h-da.de/p-eurstratnet