Arrowhead Task 1.6: Case: Mining industry condition monitoring

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Arrowhead Task 1.6: Case: Mining industry condition monitoring
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Presentation transcript:

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 (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

Outokumpu Kemi Mine 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

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

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

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

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

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

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

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

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

Thank you!