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

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