Connectionists: CFP: IoT Stream 2019: IoT Stream for Data Driven Predictive Maintenance

[Carlos Ferreira]

IoTStream 2019:IoTStream for Data Driven Predictive Maintenance
ECML-PKDD 2019
Würzburg, Germany, September 16-20, 2019

Conference website https://abifet.wixsite.com/iotstream2019 <https://abifet.wixsite.com/iotstream2019>
Submission link https://easychair.org/conferences/?conf=iotstream2019 <https://easychair.org/conferences/?conf=iotstream2019>
Abstract registration deadline June 7, 2019
Submission deadline June 7, 2019

Topics: predictive maintenance <https://easychair.org/cfp/topic.cgi?a=21955490;tid=24891> fault detection <https://easychair.org/cfp/topic.cgi?tid=2906;a=21955490> internet of things <https://easychair.org/cfp/topic.cgi?tid=200145;a=21955490> data streams <https://easychair.org/cfp/topic.cgi?tid=6455;a=21955490>

Maintenance is a critical issue in the industrial context for the prevention of high costs or injures.
The emerging technologies of Industry 4.0 empowered data production and exchange which
lead to new concepts and methodologies exploitation for maintenance. Intensive research effort
in data driven Predictive Maintenance (PdM) has been producing encouraged outcomes.
Therefore, the main objective of this workshop is to raise awareness of research trends and
promote interdisciplinary discussion in this field.


    Submission Guidelines

Regular and short papers presenting work completed or in progress are invited. Regular papers should not exceed 12 pages, while short papers are maximum 6 pages. Papers must be written in English and are to be submitted in PDF format online via the Easychair submission interface:

https://easychair.org/conferences/?conf=iotstream2019 <https://easychair.org/conferences/?conf=iotstream2019>

Each submission will be evaluated on the basis of relevance, significance of contribution, quality of presentation and technical quality by at least two members of the program committee.


    List of Topics

  *

    This workshop solicits contributions including but not limited to the following topics:

  *

    Fault Detection and Diagnosis (FDD)

  *

    Fault Isolation and Identification

  *

    Estimation of Remaining Useful Life of Components, Machines, ….

  *

    Forecasting of Product and Process Quality

  *

    Early Failure and Anomaly Detection and Analysis

  *

    Automatic Process Optimization

  *

    Self-healing and Self-correction

  *

    Incremental, evolving (data-driven and hybrid) models for FDD and anomaly detection

  *

    Self-adaptive time-series based models for prognostics and forecasting

  *

    Adaptive signal processing techniques for FDD and forecasting

  *

    Concept Drift issues in dynamic predictive maintenance systems

  *

    Active learning and Design of Experiment (DoE) aspects in dynamic predictive maintenance

  *

    Systems Fault tolerant control

  *

    Decision Support Systems for Predictive Maintenance

  *

    Data visualization for Prescriptive Maintenance

  *

    Real world applications such as:

  *

    Manufacturing systems

  *

    Production Processes and Factories of the Future (FoF)

  *

    Wind turbines (offshore/onshore/floating)

  *

    Smart management of energy demand/response

  *

    Energy and power systems and networks

  *

    Transport systems

  *

    Power generation and distribution systems

  *

    Intrusion detection and cyber security

  *

    Internet of Things,

  *

    Next Generation Airspace Applications, etc.

  *

    Big Data challenges in energy transition and digital transition

  *

    Solar plant monitoring and management

  *

    Active demand response

  *

    Distributed renewable energy management and integration into smart grids

  *

    Smart cities


    Committees


      Program Committee

  *

    Carlos Ferreira, LIAAD INESC Porto LA, ISEP, Portugal

  *

    Edwin Lughofer, Johannes Kepler University of Linz, Austria

  *

    Sylvie Charbonnier, Université Joseph Fourier-Grenoble, France

  *

    Bruno Sielly Jales Costa, IFRN, Natal, Brazil

  *

    Fernando Gomide, University of Campinas, Brazil

  *

    José A. Iglesias, Universidad Carlos III de Madrid, Spain

  *

    Anthony Fleury, Mines-Douai, Institut Mines-Télécom, France

  *

    Teng Teck Hou, Nanyang Technological University, Singapore

  *

    Plamen Angelov, Lancaster University, UK

  *

    Igor Skrjanc, University of Ljubljana, Slovenia

  *

    Indre Zliobaite, Aalto University, Austria

  *

    Elaine Faria, Univ. Uberlandia, Brazil

  *

    Mykola Pechenizkiy, TU Eindonvhen, Netherlands

  *

    Raquel Sebastião, Univ. Aveiro, Portugal


      Organizing committee

  * Rita P. Ribeiro, INESC TEC, Portugal
  *

    Sepideh Pashami, Halmstad University

  *

    Albert Bifet, Telecom-ParisTech; Paris, France

  * João Gama, INESC TEC, Portugal


    Publication

All accepted papers will be included in the workshop proceedings and will be publically available on the conference web site. At least one author of each accepted paper is required to attend the workshop to present.


    Venue

The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases will take place in Würzburg, Germany, from the *16th to the 20th of September 2019*.


    Contact

All questions about submissions should be emailed to one of the Chairs



Carlos Ferreira


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