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IOTStreams 2020 : ECML/PKDD 2020 Workshop on IoT Streams for Data-Driven Predictive Maintenance

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Link: https://abifet.wixsite.com/iotstream2020
 
When Sep 14, 2020 - Sep 14, 2020
Where Ghent, Belgium
Submission Deadline Jun 11, 2020
Notification Due Jul 20, 2020
Final Version Due Jul 27, 2020
Categories    machine learning   IOT   data streams   predictive maintenance
 

Call For Papers

2nd ECML/PKDD 2020 Workshop on

IoT Streams for Data-Driven Predictive Maintenance



ECML-PKDD 2020, September 14 –18, 2020, Ghent-Belgium

https://abifet.wixsite.com/iotstream2020


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Important dates



Workshop paper submission deadline: 11th of June 2020

Workshop paper acceptance notification: 20th of July 2020

Workshop paper camera-ready deadline: 27th of July 2020

Workshop Day: 14th of September 2020 (alternatively, 18th of September)


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Motivation and focus

Maintenance is a critical issue in the industrial context for preventing high costs
and injuries. Various industries are moving more and more toward digitalization and
collecting “big data” to enable or improve the accuracy of their predictions. At the
same time, the emerging technologies of Industry 4.0 empowered data production and
exchange, which leads to new concepts and methodologies for the exploitation of large
datasets in maintenance. The intensive research effort in data-driven Predictive
Maintenance (PdM) is producing encouraging results. Therefore, the main objective
of this workshop is to raise awareness of research trends and promote interdisciplinary
discussion in this field.

Data-driven predictive maintenance must deal with big streaming data and handle concept
drift due to both changing external conditions, but also normal wear of the equipment.
It requires combining multiple data sources, and the resulting datasets are often highly
imbalanced. The knowledge about the systems is detailed, but in many scenarios, there is
a large diversity in both model configurations, as well as their usage, additionally
complicated by low data quality and high uncertainty in the labels. In particular, many
recent advancements in supervised and unsupervised machine learning, representation
learning, anomaly detection, visual analytics and similar areas can be showcased in this
domain. Therefore, the overlap in research between machine learning and predictive
maintenance continues to increase in recent years.

This event is an opportunity to bridge researchers and engineers to discuss emerging
topics and key trends. The previous edition of the workshop at ECML 2019 has been very
popular, and we are planning to continue this success in 2020.

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Aim and scope

This workshop welcomes research papers using Data Mining and Machine Learning (Artificial
Intelligence in general) to address the challenges and answer questions related to the
problem of predictive maintenance. For example, when to perform maintenance actions, how
to estimate components current and future status, which data should be used, what decision
support tools should be developed for prognostic, how to improve the estimation accuracy
of remaining useful life, and similar. It solicits original work, already completed or in
progress. Position papers will also be considered. The scope of the workshop covers, but
is not limited to, the following:

* Predictive and Prescriptive Maintenance

* Fault Detection and Diagnosis (FDD)

* Fault Isolation and Identification

* Anomaly Detection (AD)

* Estimation of Remaining Useful Life of Components, Machines, etc.

* Forecasting of Product and Process Quality

* Early Failure and Anomaly Detection and Analysis

* Automatic Process Optimization

* Self-healing and Self-correction

* Incremental and evolving (data-driven and hybrid) models for FDD and AD

* 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) in dynamic predictive maintenance

* Industrial process monitoring and modelling

* Maintenance scheduling and on-demand maintenance planning

* Visual analytics and interactive Machine Learning

* Analysis of usage patterns

* Explainable AI for predictive maintenance

* …



It covers real-world applications such as:



* Manufacturing systems

* Transport systems (including roads, railways, aerospace and more)

* Energy and power systems and networks (wind turbines, solar plants and more)

* Smart management of energy demand/response

* Production Processes and Factories of the Future (FoF)

* Power generation and distribution systems

* Intrusion detection and cybersecurity

* Internet of Things

* Smart cities

* …


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Submission and Review process

Regular and short papers presenting work completed or in progress are invited. Regular
papers should not exceed 12 pages, while short papers are a maximum of 6 pages. Papers
must be written in English and submitted in PDF format online via the Easychair
submission interface https://easychair.org/conferences/?conf=iotstream2020.

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


----------------------------------------------------------
Important dates



Workshop paper submission deadline: 11th of June 2020

Workshop paper acceptance notification: 20th of July 2020

Workshop paper camera-ready deadline: 27th of July 2020

Workshop Day: 14th of September 2020 (alternatively, 18th of September)



The exact schedule, including time slots, will be published on the official ECML website


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Program Committee members (to be confirmed)

* Carlos Ferreira, LIAAD INESC Porto LA, ISEP, Portugal

* Edwin Lughofer, Johannes Kepler University of Linz, Austria

* Sylvie Charbonnier, Université Joseph Fourier-Grenoble, France

* David Camacho Fernandez, Universidad Politecnica de Madrid, Spain

* 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

* Slawomir Nowaczyk, Halmstad University, Sweden

* Indre Zliobaite, University of Helsinki, Finland

* Elaine Faria, Univ. Uberlandia, Brazil

* Mykola Pechenizkiy, TU Eindonvhen, Netherlands

* Raquel Sebastião, Univ. Aveiro, Portugal

* Anders Holst, RISE SICS, Sweden

* Erik Frisk, Linköping University, Sweden

* Enrique Alba, University of Málaga, Spain

* Thorsteinn Rögnvaldsson, Halmstad University, Sweden

* Andreas Theissler, University of Applied Sciences Aalen, Germany

* Vivek Agarwal, Idaho National Laboratory, Idaho

* Manuel Roveri, Politecnico di Milano, Italy

* Yang Hu, Politecnico di Milano, Italy

* Rita Ribeiro, University of Porto, Porto, Portugal


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Workshop Organizers

* Joao Gama, University of Porto, Porto, Portugal, jgama@fep.up.pt

* Albert Bifet, Telecom-ParisTech, Paris, France, albert.bifet@telecom-paristech.fr

* Moamar Sayed Mouchaweh, IMT Lille-Douai, Douai, France, moamar.sayed-mouchaweh@imt-lille-douai.fr

* Grzegorz J. Nalepa, Jagiellonian University, Krakow, Poland, gjn@gjn.re

* Sepideh Pashami, Halmstad University, Sweden, sepideh.pashami@hh.se

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