IMAACA 2019 Call For Papers
Machine Learning for Condition-Based Maintenance (ML4CBM) - Applications to Industrial Assets
Track Description
It is nowadays possible and affordable to deploy complex workflows dedicated to collect asset data, detect anomalies, and estimate remaining useful life to optimize asset maintenance. Machine Learning plays a major role in such involved workflows, leveraging the tremendous amounts of data generated by IoT sensors, maintenance systems and operating systems. It is fair to state that the availability of affordable computing power, massive data storage and low-cost sensors, initiated a revolution in condition-based maintenance.
Companies across the board have taken bold steps in implementing condition-based asset maintenance schemes as they quickly became aware of the tremendous benefits they can bring to their bottom lines.
This invited session solicits contributions describing applications of advanced Machine Learning, Big Data, Cloud Computing, and Artificial Intelligence in general to maintenance of industrial assets. Appropriate technologies for session topics include (but are not limited to):
- Image analytics.
- Aggregation of offline (non-realtime) and online methods.
- Hybrid Models (data-based, physics-based).
- Deep Learning.
- Natural Language Processing.
- Auto ML.
- RPA – Robotic Process Automation.
We look forward to receiving your contributions to the upcoming IMAACA Conference in Lisbon.
For further information please contact Daniel E. Viassolo.
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