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Predictive analytics for operations and maintenance



Our servicesServices.JPG

We provide predictive maintenance, reliability, and safety-related services for autonomous systems, machinery, vehicles and production processes. We work with both existing systems and new systems. 

 Optimized operations flow chart2.PNG

Operations and Maintenance Survey provides systematic tools for addressing the critical operations/processes/components related to production, fleet operations, and fleet maintenance actions that cause unscheduled delays.

Tailored Condition Monitoring Solutions provide reliable and cost-effective condition monitoring systems to all customer's needs, even in extreme conditions.

Data Management Solutions provide a wide range of intelligent data management tools to collect, store, manage, manipulate, and transfer machine or fleet operational data.

Health Assessment package provides robust calculation algorithms to diagnose machine faults and rate the current health of the machine considering all state information.

Remaining Useful Life Assessment determines future health states and failures modes based on the current health assessment and projected usage loads on the equipment or machine, as well as predicts the remaining useful life of the equipment or machine.

Advisory Generation package integrates all information and provides optimal recommended maintenance and operation actions, future capability assessments and strategic recommendations.


VTT Operation and maintenance Analytics O&M Analytics



Our core competencies

Our core competencies include:

​Data analytics:

  • Signal conditioning
  • Diagnostics
  • Prognostics
  • Hybrid modeling
  • Simulation
  • Cyber Physical Systems (CPS)

​Data acquisition:

  • Measurements
  • Intelligent sensor/ monitoring solutions
  • Sensor development
  • Condition-based monitoring systems (CBM)
  • Operational Loads Measurement (OLM) systems
  • Structural Health Monitoring (SHM) systems
  • Wide range of testing services

​Artificial Intelligence (AI):

  • Robotics, collaborative robots
  • Hybrid modelling, neural networks
  • Automation safety
  • Machine learning, pattern recognition
  • Data analytics

​Data management:

  • MIMOSA (Machinery Information Management Open Systems Alliance)
  • Data transfer solutions
  • Cloud solutions
  • IoT platforms
  • Web services


​Autonomous systems:

  • Concept design and development
  • Automation safety
  • Remote controlled robotics know-how
  • RAMS, reliability modelling
  • Human factors
  • Digital twin
  • Domain understanding (marine, transport, defence, working machines, pulp and paper, steel, infra, energy)
  • Retrofit to existing systems

​Special competencies:

  • Composite & light weight structures
  • Fatigue/ fracture mechanics


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

Pulp and paper

Process operation improvement

Customer: Metsä Fibre

Challenge: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 %


Metal industry

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


  • 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
  • Kunttu, Susanna, Kortelainen, Helena, Horn, Susanna. 2017. Demonstrating value with benchmarking: Omnipress Oy. Maintworld, No. 3, pp. 34-37

  • 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

  • Malm, Timo, Salmi, Timo, Marstio, Ilari, Montonen, Jari. 2017. Dynaaminen turvajärjestelmä teollisuusrobotille: Suomen Automaatioseura. Automaatioväylä, Vol. 33, No. 5, pp. 18-21

  • 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

  • Molarius, Riitta, Poussa, Liisa, Välisalo, Tero, Rosqvist, Tony, Pentikäinen, Heimo, Noponen, Sami. 2017. Teknisten järjestelmien haavoittuvuus kybermaailmassa: Turvallisuuden ja Riskienhallinnan Tietopalvelu Oy. Turvallisuus & Riskienhallinta, erikoisnumero Turvallisuus Tiede 1/2017, Vol. 38, No. 3, pp. 17-23

  • 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

  • Vepsä, Ari, Calonius, Kim, Fedoroff, Alexis, Fülöp, Ludovic, Jussila, Vilho, Saarenheimo, Arja, Varis, Piritta, Fälth, Billy, Lund, Björn, Tuomala, Markku. 2017. Experimental and numerical methods for external event assessment improving safety (ERNEST). In: SAFIR2018 - The Finnish Research Programme on Nuclear Power Plant Safety 2015-2018. Interim Report. VTT Technology: 294, VTT, pp. 89-103 ISBN 978-951-38-8524-3

  • Karhu, Marjaana, Lagerbom, Juha, Kivikytö-Reponen, Päivi, Ismailov, Arnold, Levänen, Erkki. 2017. Reaction Heat Utilization in Aluminosilicate-Based Ceramics Synthesis and Sintering: Göller Verlag. Journal of Ceramic Science and Technology, Vol. 8, No. 1, pp. 101-112 doi:10.4416/JCST2016-00094

  • Molarius, Riitta, Keränen, Jaana, Sarsama, Janne, Poussa, Liisa. 2017. Enhancing risk awareness of new and emerging technology implementation, case circular economy. In: Towards a new era in manufacturing. Final report of VTT's For Industry spearhead programme. VTT Technology: 288, VTT, pp. 165-168 ISBN 978-951-38-8514-4, 978-951-38-8513-7

  • 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. (LINK)
  • 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

  • Heikkilä, Eetu, Tuominen, Risto, Tiusanen, Risto, Montewka, Jakub, Kujala, Pentti. 2017. Safety Qualification Process for an Autonomous Ship Prototype - a Goal-based Safety Case Approach. 12th International Conference on Marine Navigation and Safety of Sea Transportation, TransNav 2017, 21 - 23 June 2017, Gdynia, Poland: CRC Press. Proceedings, pp. 365-370. ISBN 978-1-138-29762-3, 978-1-351-58219-3 doi:10.1201/9781315099132-63

  • 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

  • Stålhane, Tor, Malm, Timo. 2017. Risk assessment - Experts vs. lay people. 26th European Safety and Reliability Conference, ESREL 2016, 25 - 29 September 2016, Glasgow, United Kingdom: CRC Press. Risk, Reliability and Safety: Innovating Theory and Practice, pp. 214. ISBN 978-113802997-2

  • 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

VTT’s O&M Analytics

Building blocks for operation and maintenance: video




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


Asset Management Solutions and Services

Customer: Equipment manufacturers, operations and maintenance

Challenge: Managing fleet and machines efficiently and exploiting assets full potential. Getting most out of the investments during whole life cycle and minimising maintenance costs.

Solution: IoT/Industrial Internet based VTT's Global Asset Management Platform enable Asset/Fleet management solutions with support for decision making and analysed suggestions in real time. Together with VTT's O&M Analytics, the solution covers also Condition Based Maintenance.


  • Managing fleet and machines, exploiting assets full potential
  • Getting most out of the investments during whole life cycle and minimising maintenance costs

  • Boosting and developing service business

RAMS management process for automated machinery

Challenge: Specification of system availability requirements and the process to manage them in automated mobile machinery applications in an underground operating environment.

Solution: RAMS (Reliability, Availability, Maintainability and Safety) process specification and guidelines to implement and review RAMS activities during the system lifecycle.


  • Systematic approach and practical methodology for RAMS management for the early phases of their system development projects.
  • Methods and tools to specify system availability requirements for mobile machine applications.

  • Practices and documents that support project reviews and the demonstration of the compliance of the system design with the RAMS requirements.


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.

ArTEco: Arctic Thruster Ecosystem. ArTEco is a co-creation forum leading the research and development of ship propulsion for arctic conditions.

​See also:

Ennakoiva kunnonvalvonta varmistaa tuotantolaitoksen jatkuvan toiminnan, Promaint 1/2018, sivut 34-35.

Smart Machines and Manufacturing Competence Centre (SMACC) co-operation poster between VTT Technical Research Centre of Finland and Tampere University of Technology, 26th April 2018, about digitalizing the machines and systems through their life cycle and benefits of the data analytics:SMACC-Digital life cycle management.pdf