IoT Stream 2019 : IoT Stream 2019: IoT Stream for Data Driven Predictive Maintenance
Call For Papers
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.
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:
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:
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
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
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
Rita P. Ribeiro, INESC TEC, Portugal
Sepideh Pashami, Halmstad University
Albert Bifet, Telecom-ParisTech; Paris, France
João Gama, INESC TEC, Portugal
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.
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.
All questions about submissions should be emailed to one of the Chairs