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IoTStream 2022 : 3rd Workshop and Tutorial on IoT Streams for Data-Driven Predictive Maintenance


When Sep 19, 2022 - Sep 23, 2022
Where Grenoble, France
Submission Deadline Jun 20, 2022
Notification Due Jul 13, 2022
Final Version Due Jul 27, 2022

Call For Papers

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. 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 2020 has been very popular, and we are planning to continue this success in 2022.

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

Paper submission:
Authors should submit a PDF version in Springer LNCS style using the workshop EasyChair site: The maximum length of papers in 15 pages, including references, consistent with the ECML PKDD conference submissions.

Submitting a paper to the workshop means that if the paper is accepted, at least one author will attend the workshop and present the paper. Papers not presented at the workshop will not be included in the proceedings. We will follow ECML PKDD’s policy for attendance.

Paper publication:
Accepted papers will be published by Springer as joint proceedings of several ECML PKDD workshops.

Workshop format:
• Half-day workshop
• 1-2 keynote talks, speakers to be announced
• Oral presentation of accepted papers

Important Dates:
• Workshop paper submission deadline: June 20, 2022
• Workshop paper acceptance notification: July 13, 2022
• Workshop paper camera-ready deadline: July 27, 2022
• Workshop: September 23, 09h-12.30, 2022 (TBC)

Program Committee members (to be confirmed):
• 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
• 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

Workshop Organizers:
• Albert Bifet, Telecom-Paris, Paris, France, University of Waikato, New Zealand,
• João Gama,University of Porto, Portugal,
• Slawomir Nowaczyk, Halmstad University, Sweden,
• Carlos Ferreira, LIAAD INESC, Porto, Portugal, ISEP, Porto, Portugal,

Tutorial Organizers:
• Rita Ribeiro,University of Porto, Portugal,
• Szymon Bobek, Jagiellonian University, Poland,
• Bruno Veloso, LIAAD INESC, Porto, Portugal, University Portucalense, Porto, Portugal
• Grzegorz J. Nalepa, Jagiellonian University, Krakow, Poland,
• Sepideh Pashami, Halmstad University, Sweden,

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