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Predictive analytics for operations and maintenance




We believe

that digitalization and real-time knowledge of operation and maintenance combined with advanced analytics support sustainable and circular economy.

We aim  

for improved efficiency, quality, lifetime and reliability of machinery, vehicles and production processes.  VTT Operation and Maintenance Analytics, O&M Analytics, offers carefully developed tools to support knowledge driven performance.

We have noticed

that scientific understanding of the physical world and domain knowledge is needed to combine with data-driven analytics in order to be able to predict reliably the performance. Our expertise on data analytics, diagnostics, prognostics, condition-based monitoring, structural health monitoring and vibration control devices support hybrid and multidisciplinary approaches to develop new solutions.

We provide

smart step-by-step method to increase digital realtime knowledge. Ask more about our services: Operations and Maintenance Survey, Tailored Condition Monitoring Solutions, Data Management Solutions, Health Assessment, Remaining Useful Life Assessment and Advisory Generation  

Service packages.JPG


Examples of customer references

Simulation model estimates the added value of services

Customer: Metso Automation
Challenge: Understanding and communicating the value of provided maintenance services to the client.
Solution: System dynamic model for estimating the added value of maintenance services, and acting as a communication tool between Metso and its customers.

  • Better understanding of customer’s business and added value of services.
  • Comparison of different kind of industrial services and their added value.
  • Formulation of robust policies by locating critical areas of service systems.
  • Improved pricing and value propositions.


LEO: Lifecycle Efficiency Online

Customer: Hydroline
Challenge: Working machines containing hydraulic components operate at various environmental conditions from light to heavy use, which makes it difficult to predict the lifetime of these components. A reliable method is needed to monitor the condition of these components in order to better estimate the need for maintenance and the time to failure.
Solution: VTT developed an IoT and data analytics system for hydraulic component condition based maintenance (CBM).

  • Intelligent real time condition monitoring enables maintenance break and part utilization optimization
  • Novel service business opportunities for the customer

Process operation improvement

Customer: Metsä FibreChallenge:40% increase in cooking kappa number variation, extra cost in bleaching and increased production losses
Solution: Applying data analysis, defective equipment were found

  • Customer testimony: "Even without including wood raw material, decrease in production loss or increase in energy savings, the payback period of the problem solving project may only be a matter of days."


Pulp quality and tracking

Customer: Metsä Fibre
Challenge: Quality information from pulp is based on few samples and results are available after several days from sampling.

Solution: Development of real-time pulp quality control method. The core computational model was developed by VTT combining the theoretical domain knowledge, customer feedback and data mining.


  • Real-time quality information improves efficiency throughout the value chain
  • Material efficiency has been improved up to 2-3 %


Data Analytics in Steel Industry

Customer: SSAB, Outokumpu
Challenge: Discovery of product quality deviations early enough in strip hot rolling. Number or process variables difficult to monitor in real-time and lack of performance figures.

Solution: Customised model based online quality monitoring system that provides detailed product quality information in real-time. Tool utilises mathematical models predicting the upcoming quality and finding the root-cause for lower product quality. Tool visualises hundreds of process inputs into descriptive interactive user interface.


  • Online quality management, cost savings in early product/process failure, optimal process adjustments.
  • Developed monitoring system can be used in other hot rolling mills and other process phases.


Arctic Thruster Ecosystem (ArTEco)

Partners: Wärtsilä, ATA Gears, Klingelnberg Group, SKF, Katsa, Technische Universitat Dresden, Luleå University of Technology, Tampere University of technology, Finnish Transport Safety Agency (Trafi)

Challenge: Understanding of the thrusters operating in ice and the phenomena causing ice loads. Understanding how the loads are transferred to critical components like gears and bearings


1. Software tool to predict ice loads on thrusters and propellers.

2. Concept creation and validation for reduced loads.

3. Simulations of various thruster installations & loads.

4. New sensing, data transmission options for smart gear concept


  • Reduction of ice impact loads on thruster hubcaps by modification of the shape.
  • Continuous torsional resonant vibrations reduced with passive mass damper (ReKi)
  • New wireless measurement solutions for gear monitoring from pinion teeth through oil


  • Mejía Niño, C, Albano, M, Jantunen, E, Sharma, P, Campos, J & Baglee, D 2018, An iterative process to extract value from maintenance projects. in Proceedings of the IncoME-III 2018 conference. University of Coimbra, 3rd International Conference on Maintenance Engineering, IncoME-III 2018, Coimbra, Portugal, 6/09/18.'
  • Albano, M, Lino Ferreira, L, Di Orio, G, Malo, P, Webers, G, Jantunen, E, Gabilondo, I, Viguera, M, Papa, G & Novak, F 2018, Sensors: The Enablers for Proactive Maintenance in the Real World. in 2018 5th International Conference on Control, Decision and Information Technologies, CoDIT 2018. Institute of Electrical and Electronic Engineers IEEE, pp. 569-574, 5th International Conference on Control, Decision and Information Technologies, CoDIT 2018, Thessaloniki, Greece, 10/04/18.
  • Jantunen, E, Hooghoudt, JO, Yang, Y & McKay, M 2018, Predicting the Remaining Useful Life of Rolling Element Bearings. in Proceedings of the 2018 IEEE International Conference on Industrial Technology (ICIT). Institute of Electrical and Electronic Engineers IEEE, pp. 2035-2040, 19th IEEE International Conference on Industrial Technology, ICIT 2018, Lyon, France, 19/02/18.
  • Saarela, Olli. 2018. Ennakoiva kunnonvalvonta varmistaa tuotantolaitoksen jatkuvan toiminnan. Promaint 1/2018, sivut 34-35.
  • Jantunen, Erkki, Campos, J., Sharma, P. & Baglee, D. 2017. Digitalisation of Maintenance System Reliability and Safety (ICSRS). Institute of Electrical and Electronic Engineers IEEE, p. 343-347
  • Jantunen, Erkki, Karaila, M., Hästbacka, D., Koistinen, A., Barna, L., Juuso, E., Puñal Perreira, P., Besseau, S. & Hoepffner, J. 2017. Application system design: Maintenance. IoT Automation: Arrowhead Framework. Taylor & Francis, p. 247-280
  • Sharma, Pankaj, Baglee, David, Campos, Jaime, Jantunen, Erkki. 2017. Big Data Collection and Analysis for Manufacturing Organisations: American Institute of Mathematical Sciences. Big Data and Information Analytics, Vol. 2, No. 2, pp. 127-139 doi:10.3934/bdia.2017002
  • Campos, Jaime, Sharma, Pankaj, Gabiria, Unai Gorostegui, Jantunen, Erkki, Baglee, David. 2017. A Big Data Analytical Architecture for the Asset Management: Elsevier. Procedia CIRP, Vol. 64, pp. 369-374 doi:10.1016/j.procir.2017.03.019
  • Campos, Jaime, Sharma, Pankaj, Jantunen, Erkki, Baglee, David, Fumagalli, Luca. 2017. Business Performance Measurements in Asset Management with the Support of Big Data Technologies: De Gruyter Open. Management Systems in Production Engineering, Vol. 25, No. 3, pp. 143-149 doi:10.1515/mspe-2017-0021

  • Holmberg, Kenneth, Kivikytö-Reponen, Päivi, Härkisaari, Pirita, Valtonen, Kati, Erdemir, Ali. 2017. Global energy consumption due to friction and wear in the mining industry: Elsevier. Tribology International, Vol. 115, pp. 116-139 doi:10.1016/j.triboint.2017.05.010

  • Jantunen, Erkki, Karaila, Mika, Hästbacka, David, Koistinen, Antti, Barna, Laurentiu, Juuso, Esko, Puñal Perreira, Pablo, Besseau, Stéphane, Hoepffner, Julien. 2017. Application system design - Maintenance: Taylor & Francis Group. In: IoT Automation, Arrowhead Framework, Edited by Jerker Delsing, pp. 247-280. ISBN 978-1-4987-5675-4, 978-1-4987-5676-1 doi:10.1201/9781315367897-9

  • Viitanen, Tomi, Varis, Piritta, Siljander, Aslak. 2017. A review of aeronautical fatigue investigations in Finland March 2015 - March 2017. 35th Conference of the International Committee of Aeronautical Fatigue and Structural Integrity (ICAF), 5 - 6 June 2017, Nagoya, Japan. National Review - Finland, ICAF Doc no. 2433 . Research Report: VTT-CR-02002-17, VTT, ICAF, 61 p.


Conference presentations

  • Jantunen, Erkki. 2018. Maintenance 4.0 World of Integrated Information, Paper presented at I-ESA'18 Interoperability for Enterprise Systems and Applications, Berlin, Germany.
  • Heikkilä, Eetu. 2018. AI for Autonomous Ships – Challenges in Design and Validation. Presentation at International Seminar on Safety and Security of Autonomous Vessels (ISSAV) 2018. 21 March 2018, Delft, NL.
  • Jantunen, Erkki. et al. 2018. Prediciting the remaining useful life of rolling element bearings, Proceedings of the 2018 IEEE International Conference on Industrial Technology (ICIT), IEEE International Conference on Industrial Technology - Centre De Congres De Lyon, Lyon, France 
  • Jantunen, Erkki, Campos, Jaime, Sharma, Pankaj, Baglee, David. 2017. Digitalisation of Maintenance. 2nd International Conference on System Reliability and Safety, ICSRS 2017, 20 - 22 December 2017, Milan, Italy: Institute of Electrical and Electronics Engineers. Proceedings, pp. 343-347. ISBN 978-1-5386-3321-2

  • Jantunen, Erkki, Sharma, Pankaj, Gorostegui, Unai, Baglee, David, Campos, Jaime. 2017. Management of Maintenance on an e-Maintenance Platform. 2nd International Conference on Maintenance Engineering, IncoME-II 2017, 5 - 6 September 2017, The University of Manchester, UK: University of Manchester. Proceedings, pp. 435-446
  • Baglee, David, Gorostegui, Unai, Jantunen, Erkki, Sharma, Pankaj, Campos, Jaime. 2017. How Can SMES Adopt a New Method to Advanced Maintenance Strategies? A Case Study. 30th International Conference on Condition Monitoring and Diagnostic Engineering Management, COMADEM 2017, 10 - 13 July 2017, Preston, UK: Jost Institute for Tribotechnology, University of Central Lancashire. Proceedings, pp. 150-157. ISBN 9781909755109

  • Jantunen, Erkki, Gorostegui, Unai, Zurutuza, Urko, Larrinaga, Felix, Albano, Michele, Di Orio, Giuseppe, Malo, Pedro,, Hegedus, Csaba. 2017. The Way Cyber Physical Systems Will Revolutionise Maintenance. 30th International Conference on Condition Monitoring and Diagnostic Engineering Management, COMADEM 2017, 10 - 13 July 2017, Preston, UK: Jost Institute for Tribotechnology, University of Central Lancashire. Proceedings, pp. 448-456. ISBN 9781909755109

  • Jantunen, Erkki, Junnola, Jarno, Gorostegui, Unai. 2017. Maintenance Supported by Cyber-Physical Systems and Cloud Technology. 4th International Conference on Control, Decision and Information Technologies, CoDIT 2017, 5 - 7 April 2017, Barcelona, Spain: Faculty of Mathematics (UPC). Proceedings

Building blocks for operation and maintenance: videoDelete



VTT’s O&M Analytics

Customer: Konecranes Oy, Bronto Skylift Oy Abj
Challenge: Early detection of faults, avoiding unplanned shutdowns, optimising maintenance plans, monitoring the use profiles of a machine fleet etc. Load and use profiling, finding best practices, identifying bottlenecks, diagnostics and prognostics of fleet and machines.   
Solution: VTT's O&M Analytics software toolbox is designed for condition monitoring, diagnosis, prognostics and decision support applications. The toolbox implements both new methods and prior art and is aimed for practical data analysis work.


  • Benchmarking machines and operators, identifying best practices
  • More reliable evaluation of machine condition
  • Fleet wide optimisation and prognostics
  • Enabling Condition Based Maintenance
  • Feedback to machine design


VTT ProperScan® service (more info)

Challenge: Extreme conditions, such as high temperature and high pressure, subject production equipment to corrosion, erosion and wear that can result in failures and have far-reaching consequences. They can be difficult to detect and even harder to predict.      
Solution: VTT ProperScan® service is a collection of semi-analytical data tools and multi-technological research designed to extend the lifetime of your facility and its components.


  • Our service is designed to help you ensure operations run uninterrupted, at max efficiency.  
  • We provide an accurate action plan in time to prevent production losses


Productive 4.0: The aim is to create a user platform across value chains and industries, thus promoting the digital networking of manufacturing companies, production machines and products.

SERENA: Versatile plug-and-play platform enabling remote predictive maintenance. SERENA represents a powerful platform to aid manufacturers in easing their maintenance burdens.

MANTIS: Cyber Physical System based Proactive Collaborative Maintenance. The overall concept of MANTIS is to provide a proactive maintenance service platform architecture based on Cyber Physical Systems that allows to estimate future performance, to predict and prevent imminent failures and to schedule proactive maintenance.