ILIAS Solutions participates in the MANTIS project, which is a 30M€ ECSEL / H2020 project, executed in collaboration with 47 industrial and research partners over Europe. You can find a summary infographinc here.
The overall concept of MANTIS is to provide a proactive maintenance service platform architecture based on Cyber Physical Systems that allows for the estimation of future performance, to predict and prevent imminent failures and to schedule proactive maintenance.
Physical systems (e.g. vehicles) and the environment they operate in, are monitored continuously by a broad and diverse range of intelligent sensors, resulting in massive amounts of data that characterise the usage history, operational condition, location, movement and other physical properties of those systems. These systems form part of a larger network of heterogeneous and collaborative systems (e.g. vehicle fleets) connected via low bandwidth communication mechanisms able to operate in challenging environments.
MANTIS consists of distributed processing chains that efficiently transform raw data into knowledge while minimizing the need for bandwidth. Sophisticated distributed sensing and decision making functions are performed at different levels in a collaborative way, ranging from local nodes that locally optimize performance, bandwidth and maintenance; to cloud-based platforms that integrate information from diverse systems and execute distributed processing and analytics algorithms for global decision making. The research addressed in MANTIS will contribute to companies' assets availability, competitiveness, growth and sustainability. Use cases will be the testing ground for the innovative functionalities of the proactive maintenance service platform architecture and for its future exploitation in the industrial world. Results of MANTIS can be utilized directly in several industry areas and different fields of maintenanance.
We will focus on the real-life maintenance problem involving special purpose and off-road vehicles, where the problem consists of reducing the cost of maintenance and increasing the availability of the fleet. Using data collection and user-based annotation of problem conditions during the maintenance process, a continuous learning cycle is initiated.
In the first stage of the project we will focus on data collection and alerting users about a potential problem, using basic analysis of usage parameters and a simple rules engine.
In the second stage of the project, we will try to actually close the loop by means of failure reporting and cause analysis. This should gradually develop a repository of failure modes which can be linked to Health and Usage Monitoring data with a probability distribution.
This tight description of problem conditions and corrective actions, combined with a loose coupling with operational usage data, vehicle health data, and configuration management data will provide a basis for machine learning algorithms and predictive maintenance on a fleet-wide basis.
ACCIONA Infraestructuras S.A., ADIRA, AIT Austrian Institute of Technology GmbH, AITIA International Inc., Aalborg University, Ansaldo STS, Atlas Copco Airpower n.v. (Flanders), Budapest University of Technology and Economics, Consorzio Interuniversitario Nazionale per l'Informatica, Danfoss A/S, Fagor Arrasate S. Coop., Fortum Power and Heat Oy, Fraunhofer Institute for Experimental Software Engineering IESE, Fundacion Tekniker, Goizper S. Coop, HBM Hottinger Baldwin Messtechnik GmbH, Ikerlan S. Coop., Ilias Solutions (Brussels), Innotec, Instituto Superior de Engenharia do Porto, Instituto de Engenharia de Sistemas e Computadores do Porto , John Deere GmbH, Josef Stefan Institute, Lapland University of Applied Sciences, Mondragon Corporation Cooperativa, Mondragon Goi Eskola Politeknikoa S. Coop., Mondragon Sistemas, Nome Oy, Philips, Philips Consumer Lifestyle B.V., Philips Electronics Nederland B.V., Philips Medical Systems Nederland B.V., Rijksuniversiteit Groningen, Robert Bosch GmbH, STILL GmbH, Science and Technology B.V., Sirris c.d.g. (Flanders+Brussels), Solteq Oy, Technische Universiteit Eindhoven, UNINOVA, VTT, Wapice, XLAB, m2Xpert GmbH & Co KG