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.
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.
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.
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.
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.
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.
WP6, led by Emphasis DigiWorld, deals with the commercial exploitation of the edge solution.
WP7, led by BIBA, deals with dissemination activities, which focus on translating the technologies into learning nuggets.
WP8, led by LMS, deals with the overall coordination and management of the project, both financial and technical.
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