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Arrowhead Task 1.6: Case: Mining industry condition monitoring 1 Mika Karaila, D.Sc. (Tech.) Research Manager

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Presentation on theme: "Arrowhead Task 1.6: Case: Mining industry condition monitoring 1 Mika Karaila, D.Sc. (Tech.) Research Manager"— Presentation transcript:

1 Arrowhead Task 1.6: Case: Mining industry condition monitoring 1 Mika Karaila, D.Sc. (Tech.) Research Manager

2 2 WP 1.6 Participants (FINLAND) -Metso Automation (task leader)  Mika Karaila, Yiqing Liang -Outokumpu  Petri Vuolukka, Pasi Lassuri -VTT Technical Research Centre of Finland  Erkki Jantunen, Ventä Olli, Määttä Kalle -Wapice Ltd.  Laurentiu Barna, Veli-Pekka Salo, Pasi Tuominen -Tampere University of Technology  David Hästbacka, Seppo Kuikka -University of Oulu  Esko Juuso, Antti Koistinen, Jouni Laurila

3 3 Outokumpu Multiple challenges in mining:  Optimal and correct system operation o Reduced risks o Remote control  Large number of devices from different vendors o Cost-effective on-time maintenance o Maintenance strategy o Condition monitoring of devices and equipment  ERP integration Kemi Mine

4 4 STAVANGER OPC UA Server OPC UA Clients Generic Data Model TAMPERE UNIVERSIT Y OF TECHNOL OGY Information Services for Condition Monitoring and Maintenance  Data Aggregation and Unified Access  Information Model and Interoperability  Events and Notifications  OPC UA Client/Server Architecture WAPICE REMOTE MANAGEMENT (WRM)  OPC UA Servers / Clients  Generic Data Model & Databases  Terminal Communication  REST API  VPN, Security Gateway WRM TERMINAL (WRM247+)  Data Acquisition  Device Control  Accelerometer, GPS  RS-232, RS-485, USB  Digital I/O, Analog Output  1-Wire, CAN  Ethernet, GPRS, 3G WRM Desktop  User Interface (web based) WP 1.6 Demonstration Automation System  Metso DNA Beckhoff  OPC UA Server Beaglebone Black  OPC UA Server OPC UA (alarms & events) Generic Information Model process and control data (e.g. from/to the Kemi mine) OPC UA process and control data (e.g. from/to the Kemi mine) OPC UA process and control data (e.g. from/to the Kemi mine) TAMPERE KEMI ESPOO OULU  VTT Node (Acceleration Sensor)  Acceleration Data  MIMOSA condition monitoring data (from the Kemi mine) VAASA Enterprise Applications and Mobile Clients  Condition monitoring  Condition and stress indexes  Vibration analysis condition monitoring data (from the Kemi mine)  Grinding mill (Kemi)  Wear part monitoring  Hoisting rope damage  Fine concentrate machine vision

5 Demo session: Metso 5 BeagleBone Black: OPC UA server Node-red: Sensortag OPC UA client Raspberry PI: Camera Node-red: Cloud storage Windows: OPC UA client Cloud: Big Data Beckhoff PLC: OPC UA server

6 Demo session: Wapice 6 Wapice Remote Management (WRM) System, OPC UA

7 Demo session: Tampere University of Technology 7 OPC UA based aggregation of heterogeneous device data for maintenance information systems Dynamic system structure enables scalability to data gathering and propagation of event notifications from a multitude of devices OPC UA information modeling for declaring data relations and semantics as well as views for different purposes Consolidating information model e.g. for device, segment or site level services (i.e. Arrowhead framework) Adaptation of legacy system structures for improved interoperability Built-in support for information security for a multi-vendor environment

8 Demo session: VTT Technical Research Centre of Finland 8 CMMS: Registry Work management VTT Little Node: Vibration acceleration data Wear plate monitoring Windows: Mimosa Cloud: Big Data Maintenance centre: Wear plate diagnosis

9 Preliminary results from the first test 9 Change of natural frequency (1050 -> 710 Hz) of a wear plate during a 2 month period 19.4–

10 Demo session: VTT Technical Research Centre of Finland 10 Wireless data acquisition system using BT-LE sensor nodes, smartphones and gateway units. Intelligent distribution of data pre-processing in node, in phone and in gateway- nodes to optimize energy, bandwidth and capacity usage Use case/next steps: Implementation of distributed data analyzing system for wear plate analysis utilizing distributed WSN architecture Distributed analysis in WSNSmartphone data analysis

11 Demo session: University of Oulu, Overview On-site data processing FFT  Derivation  IFFT  Norms and describing indices 11 Finding the degrees Matlab demonstration Location and setup

12 12 Thank you!


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