Special Session: XPdM 2021 - Data-Driven Predictive Maintenance for Industry 4.0


IEEE DSAA 2021, October 06-09, 2021, Porto-Portugal

https://sites.google.com/g.uporto.pt/ddpdm2021/home


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Important dates


* Special session paper submission deadline: 06th of June 2021
* Special session paper acceptance notification: 25th of July 2021
* Special session paper camera-ready deadline: 8th of August 2021

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Motivation and focus

The amount of data generated by industrial processes has increased exponentially due to the usage of monitoring systems and sensing technologies, which are emerging technologies of Industry 4.0. The data produced can lead to new concepts and exploitation methodologies that can be used to analyze and extract valuable knowledge about the industrial process or equipment.

Maintenance is a critical issue in the industrial context, medical equipment, energy systems, passengers transport vehicles and home appliances, among others. The prevention of high costs, the avoidance of disruptions on the operation time of equipment, its efficiency and safety, have been the main concerns of companies and organizations. Various industries are moving towards digitalization and collecting “big data” to enable or improve the accuracy of their prediction. Predictive Maintenance (PdM) is a prominent approach to deal with maintenance issues. Promising data-driven methodologies for predictive maintenance are emerging as a compelling alternative.

Data-driven predictive maintenance deals with big streaming data requiring the combination of multiple data sources resulting in datasets, which are often highly imbalanced. Moreover, it involves the ability to handle concept drift due to both changing external conditions, but also normal wear of the equipment. 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, recent advancements, both in supervised and unsupervised machine learning, representation learning, anomaly detection, visual analytics and similar areas can be showcased in this domain.

Over the last years, an intensive research effort on the PdM topic has been producing encouraging outcomes. Therefore, the main objective of this Special Session is to raise awareness of research trends and promote interdisciplinary discussion in this field.

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Topics of interest

Topics of interest for the special session include, but are not limited to:

* Explainable AI for predictive maintenance
* Fault Detection and Diagnosis (FDD)
* Fault Isolation and Identification
* Estimation of Remaining Useful Life of Components and Machines
* Forecasting of Product and Process Quality
* Early Failure and Anomaly Detection and Analysis
* Automatic Process Optimization
* Predictive and prescriptive maintenance
* 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) in dynamic predictive maintenance
* Fault-tolerant control systems
* Industrial process monitoring and modelling
* Maintenance scheduling and on-demand maintenance planning
* Visual analytics and interactive Machine Learning
* Decision-making assistance and resource optimization
* Planning under uncertainty
* Analysis of usage patterns

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 cybersecurity
* Internet of Things
* Next-Generation Aerospace Applications
* 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

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Submission and Review process

The paper length allowed for the papers in the Research and Application tracks is a maximum of ten (10) pages.

The format of papers is the standard 2-column U.S. letter style IEEE Conference template. See the IEEE Proceedings Author Guidelines: https://www.ieee.org/conferences/publishing/templates.html for further information and instructions.

All submissions will be blind reviewed by the Program Committee on the basis of technical quality, relevance to the conference’s topics of interest, originality, significance, and clarity. Author names and affiliations must not appear in the submissions, and bibliographic references must be adjusted to preserve author anonymity. Submissions failing to comply with paper formatting and authors anonymity will be rejected without reviews.

Because of the double-blind review process, non-anonymous papers that have been issued as technical reports or similar cannot be considered for DSAA’2021. An exception to this rule applies to arXiv papers that were published in arXiv at least a month prior to DSAA’2021 submission deadline. Authors can submit these arXiv papers to DSAA provided that the submitted paper’s title and abstract are different from the one appearing in arXiv. Papers that appear in arXiv from the DSAA’2021 submission deadline until the review process has ended, will be rejected without reviews.

Authors are also encouraged to support their papers by providing through a git-type public repository the code and data to support the reproducibility of their results.

Submissions to the Special Session, should be made to CMT: https://cmt3.research.microsoft.com/DSAA2021


** Extended versions of best selected papers will be published on:
https://www.mdpi.com/journal/sensors/special_issues/predictive_maintenance_sensor **

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Special Session Organizers

* Bruno Veloso, University Portucalense & INESCTEC, Porto, Portugal
* Grzegorz J. Nalepa, Jagiellonian University, Krakow, Poland
* Moamar Sayed Mouchaweh, IMT Lille-Douai, Douai, France
* Rita P. Ribeiro, University of Porto & INESCTEC, Porto, Portugal
* Sepideh Pashami, Halmstad University, Sweden
* Slawomir Nowaczyk, Halmstad University, Sweden