AssetUp4.0 Project
AssetUp4.0 is an EIT-Manufacturing project, aims to early detect forthcoming asset critical malfunction and provides a preventive maintenance framework that allow for timely scheduling of maintenance actions. The focus relies on development of edge intelligence for condition monitoring and status visibility of assets in harsh industrial environments.
AssetUp4.0 will utilize results developed under three EU-H2020 R&D projects. Emphasis Digiworld has developed a multi-sensor device to be used for gasses measurement in harsh ore mining environment under the DIATOMIC project. BIBA has developed predictive maintenance modules under the UPTIME project. LMS has worked in extracting machine status related information from sensor data in the ICP4Life project and on condition monitoring from sensor data in the SERENA project.
The AssetUp4.0 solution offers asset operational visibility and monitoring of maintenance critical parameters and will be field tested on Aluminium of Greece’s plant in the ore processing section for milling.
AssetUP4.0 Platform
AssetUp4.0 Platform provides data visualization including a layer for providing intelligence and decision support to preventive maintenance processes.
The Platform includes IIoT hardware components specifically adapted for the harsh industrial environment that include sensing elements for acceleration, vibration, gyroscope, sound, lubrication flow, power consumption, etc. The IIoT unit integrates enhanced processing capability in order to provide the required edge intelligence and data filtering and aggregation. The data communications function is specifically adapted to the industrial environment and throughput capacity requirements.
Smart Sensing for Asset Management
An Edge device for sensing and assessing asset’s status in harsh environments will be designed and developed. An IIoT approach will be adopted, including wireless data connectivity and smart sensing of asset behaviour. Asset data will include vibration, temperature, flow rate, noise, working status as well as other asset functional parameters. The IIoT edge solution will include intelligence in order to reduce data communication requirements and support promptly exceptions. The edge solution, in particular the communication protocol and data wireless connectivity, will be designed for potential extension in order to support requirements of all plant assets of the same type.
Condition Monitoring and Predictive Maintenance
The health condition monitoring and predictive maintenance component developed in the UPTIME project is able to record and evaluate sensor data by applying AI algorithms to the component. It allows the engineer to apply AI methods to sensor data without actually having inside to it and without any programming knowledge. The component will be adapted to the Edge Computing paradigm in order to be able to manage Edge devices and be able to monitor predictions in one place. The ability to manage Edge devices in terms of uploading new mathematical models to them, being able to divide the devices in domains, topics and assets will be developed in AssetUp4.0.
Assessment of Production Status from Smart Sensor
The production status assessment component will further developed, based on the ICP4LIFE planner previously developed for optimizing production activities according to real-world conditions evaluated through sensor data and using semantic technologies. The component utilizing data from the sensor will provide insight into the status of the production in the asset. Creation of a system able to measure the mill status of Aluminium of Greece will be the main focus. By measuring the real-time sensor data such as vibration, an accurate status signal can be obtained. This status signal can be used to run the equipment under automated loop control or greatly assist control room operations under manual control.
Expected Impacts
Metal industry needs to introduce new methods in order to reduce production stoppage due to asset downtime. Seven mills of the Aluminium of Greece plant work under extremely harsh conditions and are exposed to quite frequent failures, mostly due to mechanical nature. The mill machine stoppage directly affects material flaw within the aluminium production line & creates major disturbance of the production process. Although already established preventive maintenance operations aim to reduce mill machine stoppage, there are still some unexpected major breakdowns of the mills.
The pilot is perfectly suitable for tuning & field test of the AssetUp4.0 solution due to the integrated edge intelligence that will provide the required multi parameter assessment. The Aluminium of Greece is interested to control the mill operation taking under consideration aspects such as the mill fill level (%). The anticipated mill machine downtime reduction is at least 5%.
Work Packages
WP 1
Use Case Definition, Demonstration and Evaluation
WP1, led by Aluminium of Greece, deals with definition of the industrial use case of Aluminium of Greece. It includes the detailed functional and technical requirements deriving from the described use case along with the definition of the validation criteria for the AssetUp4.0 application.
WP 2
Smart Sensing for Asset Management and Integration
WP2, led by Emphasis DigiWorld, deals with design and development of the edge device for sensing and assessing asset’s status in harsh environments. Emphasis DigiWorld will built upon its multi-sensor device developed under the DIATOMIC Sense&Mine4.0 project for measurement of gasses concentration and monitoring in harsh ore mining environment.
WP 3
Condition Monitoring and Predictive Maintenance Component
WP3, led by BIBA, deals with adaption of its health condition monitoring and predictive maintenance component from the UPTIME project, which has been tested in various industrial use cases, to the Edge Computing paradigm in order to manage edge devices and be able to monitor predictions in one place.
WP 4
Assessment of Production Status from Smart Sensor Data
WP4, led by LMS, deals with development of production status assessment component. The component, utilizing data from the sensor, provides insight into the status of the production in the asset. LMS will use the ICP4LIFE Planner and its semantic technologies as a basis for the development to assess the production status evaluating sensor data coming from WP2 along with predictive analytics generated by WP3.
WP 5
Installation, Demonstration, and Validation
WP5, led by Aluminium of Greece deals with installation, demonstration and validation of the AssetUp4.0 solution. Firstly, off-the-shelve sensors will be installed in the industrial assets to collect data for different status of the asset. The data will be used for training data in WP3 and WP4. Secondly, the edge sensor equipped with edge analytics will be installed in the production area in order for the solution to be validated in the TRL8 environment.
WP 6
Exploitation
WP6, led by Emphasis DigiWorld, deals with the commercial exploitation of the edge solution.
WP 7
Dissemination
WP7, led by BIBA, deals with dissemination activities, which focus on translating the technologies into learning nuggets.
WP 8
Project Management
WP8, led by LMS, deals with the overall coordination and management of the project, both financial and technical.
Consortium
Laboratory for Manufacturing Systems and Automation® of the Department of Mechanical Engineering and Aeronautics, in the University of Patras, Greece, is oriented on research and development in cutting edge scientific and technological fields.
BIBA – Bremer Institut für Produktion und Logistik GmbH is an engineering research institute developing technical and organizational solutions for production and logistics in close cooperation with industry.
Emphasis Digiworld SA focuses on the development of solutions for data-driven operational intelligence and sensor-based performance analysis of business processes.
Aluminium of Greece is an aluminium producing company in Greece and remains one of the strongest pillars of Greek industry, with an annual production capacity that exceeds 182,000 tons of aluminium and 820,000 tons of alumina.
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