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Arrowhead Task 1.6: Case: Mining industry condition monitoring

Arrowhead Task 1.6: Case: Mining industry condition monitoring. Mika Karaila , D.Sc . (Tech .). Research Manager. mika.karaila@metso.com, +358 40 761 2563. WP 1.6 Participants (FINLAND). Metso Automation ( t ask leader) Mika Karaila , Yiqing Liang Outokumpu

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Arrowhead Task 1.6: Case: Mining industry condition monitoring

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  1. Arrowhead Task 1.6: Case: Mining industry condition monitoring Mika Karaila, D.Sc. (Tech.) ResearchManager mika.karaila@metso.com, +358 40 761 2563

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

  4. WP 1.6 Demonstration condition monitoring data (from the Kemi mine) • Grinding mill (Kemi) • Wear part monitoring • Hoisting rope damage • Fine concentrate machine vision • Condition monitoring • Condition and stress indexes • Vibration analysis • Automation System • Metso DNA • Beckhoff • OPC UA Server • Beaglebone Black • OPC UA Server • Information Services for Condition Monitoring and Maintenance • Data Aggregation and Unified Access • Information Model and Interoperability • Events and Notifications • OPC UA Client/Server Architecture TAMPERE UNIVERSITY OF TECHNOLOGY KEMI OPC UA Server OPC UA Clients Generic Data Model OULU process and control data (e.g. from/to the Kemi mine) condition monitoring data (from the Kemi mine) VAASA TAMPERE OPC UA process and control data (e.g. from/to the Kemi mine) • 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) ESPOO STAVANGER • VTT Node (Acceleration Sensor) • Acceleration Data • MIMOSA OPC UA process and control data (e.g. from/to the Kemi mine) Enterprise Applications and Mobile Clients OPC UA (alarms & events) Generic Information Model

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

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

  7. Demo session: Tampere University of Technology 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 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 OPC UA information modeling for declaring data relations and semantics as well as views for different purposes

  8. Demo session: VTT Technical Research Centre of Finland Cloud: Big Data Windows: Mimosa Maintenancecentre: Wearplatediagnosis CMMS: Registry Work management VTT Little Node: Vibrationacceleration data Wearplatemonitoring

  9. Preliminary results from the first test Change of natural frequency (1050 -> 710 Hz) of a wear plate during a 2 month period 19.4–24.5.2014

  10. Demo session: VTT Technical Research Centre of Finland 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 WSN Smartphone data analysis

  11. Demo session: Universityof Oulu, Overview On-site data processing FFTDerivationIFFTNorms and describingindices Location and setup Findingthedegrees Matlabdemonstration

  12. Thank you!

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