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Furtwangen University of Applied Sciences, Germany

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33 Papers

Machine Learning Models with Fault Tree Analysis for Explainable Failure Detection in Cloud Computing

Topics: Service Management; Services Security and Reliability; Cloud Application Scalability and Availability; Cloud Quality and Performance; Security, Privacy and Compliance Management

Authors: Rudolf Hoffmann and Christoph Reich

Affiliation: Institute for Data Science, Cloud Computing and IT Security, Furtwangen University, Germany

Digital Object Identifier: 10.5220/0012727600003711

Proceedings of CLOSER 2024, ISBN: 978-989-758-701-6

Quick Abstract

Cloud computing infrastructures availability rely on many components, like software, hardware, cloud management system (CMS), security, environmental, and human operation, etc. If something goes wrong the root cause analysis (RCA) is often complex. This paper explores the integration of Machine Learning (ML) with Fault Tree Analysis (FTA) to enhance explainable failure detection in cloud computing systems. We introduce a framework employing ML for FT selection and generation, and for predicting Basic Events (BEs) to enhance the explainability of failure analysis. Our experimental validation focuses on predicting BEs and using these predictions to calculate the Top Event (TE) probability. The results demonstrate improved diagnostic accuracy and reliability, highlighting the potential of combining ML predictions with traditional FTA to identify root causes of failures in cloud computing environments and make the failure diagnostic more explainable.

ARTHUR: Machine Learning Data Acquisition System with Distributed Data Sensors

Topics: Industrial Applications of Services Science; Time Series Services: Databases, Dashboards; Edge Cloud Network Management; IoT-Cloud Integration; AI-enabled Edge Cloud

Authors: Niels Schneider , Philipp Ruf , Matthias Lermer and Christoph Reich

Affiliation: Institute for Data Science, Cloud Computing and IT Security, Furtwangen University of Applied Science, Robert-Gerwig-Platz 1, 78120 Furtwangen im Schwarzwald, Germany

Digital Object Identifier: 10.5220/0011747100003488

Proceedings of CLOSER 2023, ISBN: 978-989-758-650-7

Quick Abstract

On the way to the smart factory, the manufacturing companies investigate the potential of Machine Learning approaches like visual quality inspection, process optimisation, maintenance prediction and more. In order to be able to assess the influence of Machine Learning based systems on business-relevant key figures, many companies go down the path of test before invest. This paper describes a novel and inexpensive distributed Data Acquisition System, ARTHUR (dAta collectoR sysTem witH distribUted sensoRs), to enable the collection of data for AI-based projects for research, education and the industry. ARTHUR is arbitrarily expandable and has so far been used in the field of data acquisition on machine tools. Typical measured values are Acoustic Emission values, force plate X-Y-Z force values, simple SPS signals, OPC-UA machine parameters, etc. which were recorded by a wide variety of sensors. The ARTHUR system consists of a master node, multiple measurement worker nodes, a local streaming system and a gateway that stores the data to the cloud. The authors describe the hardware and software of this system and discuss its advantages and disadvantages.

Leveraging Web Components for Authoring Interactive Mathematics

Topic: Authoring Tools and Content Development

Authors: Rudolf Hoffmann

Affiliation: Furtwangen University, Germany

Digital Object Identifier: 10.5220/0011946600003470

Proceedings of CSEDU 2023, ISBN: 978-989-758-641-5

Quick Abstract

Digital, interactive content can support active learning and provide both motivation and an automated feedback to students. Unfortunately authoring interactive content remains a difficult task for teachers. There are few interoperable standards and e-learning platforms often restrict what is technically possible. Even if some teachers create amazing interactive content it is challenging to publish and share this content with colleagues. This paper proposes a way to leverage \textit{Web Components}, a rather new technology supported by modern browsers, to allow teachers to quickly author interactive exercises involving mathematical expressions and visualizations. As \textit{Web Components} are standardized they can be shared and embedded in any website. The proposed \textit{Web Components} for mathematics allow for: 1. A convenient visual input method for expressions and formulas, 2. A sophisticated validation system for these expressions in order to give immediate feedback to students on their solutions.

Results from using an Automl Tool for Error Analysis in Manufacturing

Topics: Industrial Applications of Artificial Intelligence; Data Mining; Big Data

Authors: Alexander Gerling ^{1

;

2

;

3} , Oliver Kamper ⁴ , Christian Seiffer ¹ , Holger Ziekow ¹ , Ulf Schreier ¹ , Andreas Hess ¹ and Djaffar Ould Abdeslam ^{2

;

3}

Affiliations: ¹ Business Information Systems, Furtwangen University of Applied Science, 78120 Furtwangen, Germany , ² IRIMAS Laboratory, Université de Haute-Alsace, 68100 Mulhouse, France , ³ Université de Strasbourg, France , ⁴ SICK AG, 79183 Waldkirch, Germany

Digital Object Identifier: 10.5220/0010998100003179

Proceedings of ICEIS 2022, ISBN: 978-989-758-569-2

Quick Abstract

Machine learning (ML) is increasingly used by various user groups to analyze product errors with data recorded during production. Quality engineers and production engineers as well as data scientists are the main users of ML in this area. Finding a product error is not a trivial task due to the complexity of today’s production processes. Products have often many features to check and they are tested in various stages in the production line. ML is a promising technology to analyze production errors. However, a key challenge for applying ML in quality management is the usability of ML tools and the incorporation of domain knowledge for non-experts. In this paper, we show results from using our AutoML tool for manufacturing. This tool makes the use of domain knowledge in combination with ML easy to use for non-experts. We present findings obtained with this approach along with five sample cases with different products and production lines. Within these cases, we discuss the occurred error origins that were found and show the benefit of a supporting AutoML tool.

Evaluation of Visualization Concepts for Explainable Machine Learning Methods in the Context of Manufacturing

Topics: Machine Learning; User Experience Design; Design and Evaluation; User Experience Evaluation

Authors: Alexander Gerling ^{1

;

2

;

3} , Christian Seiffer ¹ , Holger Ziekow ¹ , Ulf Schreier ¹ , Andreas Hess ¹ and Djaffar Ould Abdeslam ^{2

;

3}

Digital Object Identifier: 10.5220/0010688900003060

Proceedings of CHIRA 2021, ISBN: 978-989-758-538-8

Quick Abstract

Machine Learning (ML) is increasingly used in the manufacturing domain to identify the root cause of product errors. A product error can be difficult to identify and most ML models are not easy to understand. Therefore, we investigated visualization techniques for use in manufacturing. We conducted several interviews with quality engineers and a group of students to determine the usefulness of 15 different visualizations. These are mostly state-of-the-art visualizations or adjusted visualizations for our use case. The objective is to prevent misinterpretations of results and to help making decisions more quickly. The most popular visualizations were the Surrogate Decision Tree Model and the Scatter Plot because they show simple illustrations that are easy to understand. We also discuss eight combinations of visualizations to better identify the root cause of an error.

A Reference Process Model for Machine Learning Aided Production Quality Management

Topics: Industrial Applications of Artificial Intelligence; Operational Research

Authors: Alexander Gerling ¹ , Ulf Schreier ² , Andreas Hess ² , Alaa Saleh ¹ , Holger Ziekow ² and Djaffar Ould Abdeslam ³

Affiliations: ¹ Business Information Systems, Furtwangen University of Applied Science, 78120 Furtwangen, Germany, IRIMAS Laboratory, Université de Haute-Alsace, 68100 Mulhouse, France, Université de Straßbourg, France , ² Business Information Systems, Furtwangen University of Applied Science, 78120 Furtwangen, Germany , ³ IRIMAS Laboratory, Université de Haute-Alsace, 68100 Mulhouse, France, Université de Straßbourg, France

Digital Object Identifier: 10.5220/0009379705150523

Proceedings of ICEIS 2020, ISBN: 978-989-758-423-7

Quick Abstract

The importance of machine learning (ML) methods has been increasing in recent years. This is also the reason why ML processes in production are becoming more and more widespread. Our objective is to develop a ML aided approach supporting production quality. To get an overview, we describe the manufacturing domain and use a visualization to explain the typical structure of a production line. Within this section we illustrate and explain the as-is process to eliminate an error in the production line. Afterwards, we describe a careful analysis of requirements and challenges for a ML system in this context. A basic idea of the system is the definition of product testing meta data and the exploitation of this knowledge inside the ML system. Also, we define a to-be process with ML system assistance for checking production errors. For this purpose, we describe the associated actors and tasks as well.

Combining Evidential Clustering and Ontology Reasoning for Failure Prediction in Predictive Maintenance

Topics: Semantic Web; Machine Learning; Knowledge Representation and Reasoning; Uncertainty in AI ; Ontologies

Authors: Qiushi Cao ¹ , Ahmed Samet ² , Cecilia Zanni-Merk ¹ , François Bertrand de de Beuvron ² and Christoph Reich ³

Affiliations: ¹ Normandie Université/INSA Rouen, LITIS, 76000 Saint- Étienne-du-Rouvray, France , ² ICUBE/SDC Team (UMR CNRS 7357)-Pole API BP 10413, 67412 Illkirch, France , ³ Hochschule Furtwangen, 78120 Furtwangen, Germany

Digital Object Identifier: 10.5220/0008969506180625

Proceedings of ICAART 2020, ISBN: 978-989-758-395-7

Quick Abstract

In smart factories, machinery faults and failures are detrimental to the efficiency and reliability of production systems. To ensure the smooth operation of production systems, predictive maintenance techniques have been widely used in a variety of contexts. In this paper, we tackle the machinery failure prediction task by introducing a novel hybrid ontology-based approach. The proposed approach is based on the combined use of evidential theory tools and semantic technologies. Among evidential theory tools, the Evidential C-means (ECM) algorithm is used to assess the criticality of failures according to two main parameters (time constraints and maintenance cost). In addition, domain ontologies with their rule-based extensions are used to formalize the domain knowledge and predict the time and criticality of future failures. Case studies on synthetic data sets and a real-world data set are used to validate the proposed approach.

A Fog-Cloud Computing Infrastructure for Condition Monitoring and Distributing Industry 4.0 Services

Topics: Edge Cloud Orchestration; Cluster Management; Edge Cloud Network Management; Edge Cloud Data Management; IoT-Cloud Integration

Authors: Timo Bayer , Lothar Moedel and Christoph Reich

Affiliation: Institute of Cloud Computing and IT Security, University of Applied Science, 78120 Furtwangen and Germany

Digital Object Identifier: 10.5220/0007584802330240

Proceedings of CLOSER 2019, ISBN: 978-989-758-365-0

Quick Abstract

Data-driven Industry 4.0 applications require low latency data processing and reliable communication models to enable efficient operation of production lines, fast response to failures and quickly adapt the manufacturing process to changing environmental conditions. Data processing in the Cloud has been widely accepted and in combination with Fog technologies, it can also satisfy these requirements. This paper investigates the placement of service containers and wheater they should be carried out in the Cloud or at a Fog node. It shows how to provide an uniform well-monitored execution environment to automatically distribute services concerning their application-specific requirements. An infrastructure is presented, that utilizes measurement probes to observe the node and environmental conditions, derive and evaluate appropriate distribution algorithms and finally deploy the application services to the node that meets the requirements.

Privacy Enhancing Data Access Control for Ambient Assisted Living

Topics: Service Platforms; Privacy, Security and Trust; Access Control; Monitoring of Services, Quality of Service, Service Level Agreements

Authors: Hendrik Kuijs ¹ , Timo Bayer ¹ , Christoph Reich ¹ , Martin Knahl ¹ and Nathan Clarke ²

Affiliations: ¹ Institute for Cloud Computing and IT Security, Furtwangen University, Furtwangen and Germany , ² Centre for Security, Communications and Network Research, Plymouth University, Plymouth and U.K.

Digital Object Identifier: 10.5220/0007735804480455

Proceedings of CLOSER 2019, ISBN: 978-989-758-365-0

Quick Abstract

As private data is key to applications in the field of Ambient Assisted Living, access control has to be in place to regulate data flows within the environment and to preserver the privacy of a user. We present a data access control system based on an easy to understand policy language with the ability to be extended by context information. Context information are enabling applications in the field of Ambient Assisted Living to adapt their behaviour to temporal, emergency or environmental conditions. The system is able to monitor and control data flows in OSGi environments by proxy services, without the need of modifying the core platform or the service logic of bundles. This is necessary to inform data subjects, to enable the data subject to control the environment and to enforce data access policies that are compatible with legal requirements.

Rule-based Security Monitoring of Containerized Workloads

Topics: Privacy, Security and Trust; High Performance Computing Cloud Applications ; Use Cases , Experiences with HPC Clouds; Security, Privacy and Compliance Management; Virtualization Technologies; Application Containers

Authors: Holger Gantikow ¹ , Christoph Reich ² , Martin Knahl ³ and Nathan Clarke ⁴

Affiliations: ¹ Science+Computing ag, Atos, Tuebingen and Germany , ² Institute for Cloud Computing and IT Security, Furtwangen University, Furtwangen, DE and Germany , ³ Faculty of Business Information Systems, Furtwangen University, Furtwangen, DE and Germany , ⁴ Center for Security, Communications and Network Research, Plymouth University, Plymouth and U.K.

Digital Object Identifier: 10.5220/0007770005430550

Proceedings of CLOSER 2019, ISBN: 978-989-758-365-0

Quick Abstract

In order to further support the secure operation of containerized environments and to extend already established security measures, we propose a rule-based security monitoring, which can be used for the detection of a variety of misuse and attacks. The capabilities of the open-source tools used to monitor containers are closely examined and the possibility of detecting undesired behavior is evaluated on the basis of various scenarios. Further, the limits of the approach taken and the associated performance overhead will be discussed. The results show that the proposed approach is effective in many scenarios and comes at a low performance overhead cost.