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  • 1.
    Lejon, Erik
    et al.
    Gestamp HardTech AB.
    Kyösti, Petter
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, ProcessIT Innovations R&D Centre.
    Lindström, John
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, ProcessIT Innovations R&D Centre.
    Machine learning for detection of anomalies in press-hardening: Selection of efficient methods2018In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 1079-1083Article in journal (Refereed)
    Abstract [en]

    The paper addresses machine learning methods, utilizing data from industrial control systems, that are suitable for detecting anomalies in the press-hardening process of automotive components. The paper is based on a survey of methods for anomaly detection in various applications. Suitable methods for the press-hardening process are implemented and evaluated. The result shows that it is possible to implement machine learning for anomaly detection by non-machine learning experts utilizing readily available programming libraries/APIs. The three evaluated methods for anomaly detection in the press-hardening process all perform well, with the autoencoder neural network scoring highest in the evaluation.

  • 2.
    Lindström, John
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, ProcessIT Innovations R&D Centre. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Digitalisering för företag inom tillverknings- och processindustrin: Visionärt kunskapsunderlag till den regionala digitala agendan2016Report (Other academic)
  • 3.
    Lindström, John
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, ProcessIT Innovations R&D Centre. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Eliasson, Jens
    ThingWave AB.
    Hermansson, Anders
    BnearIT AB.
    Blomstedt, Fredrik
    BnearIT AB.
    Kyösti, Petter
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, ProcessIT Innovations R&D Centre. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Cybersecurity level in IPS2: A case study of two industrial internet-based SME offerings2018In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 73, p. 222-227Article in journal (Refereed)
    Abstract [en]

    n a case study comprising two SMEs offering Industrial Product-Service Systems (IPS2) based on the industrial internet the paper addresses the current cybersecurity level of the two SMEs and the perceived need for added cybersecurity features. Cybersecurity is of crucial importance for most IPS2-offerings if they involve data communications, data collection and storage, and are also part of the customers’ critical processes (i.e., the core processes that always need to work with a high level of availability). The case study reveals that both IPS2-offerings have a basic level of core security spanning IoT-devices, IoT-networks, cloud services and users as well as administrators. Further, of interest is that the SMEs would like to add security cloud services with advanced security functionality in order to achieve scalability and efficiency regarding security- and general management as well as lifecycle management functionality. However, most of the security cloud services are mainly aimed at larger companies and not adapted for SMEs in terms of required knowledge, time and effort required to keep the security configurations up-to-date.

  • 4.
    Lindström, John
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, ProcessIT Innovations R&D Centre.
    Kyösti, Petter
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, ProcessIT Innovations R&D Centre.
    Delsing, Jerker
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Ventä, Olli (Contributor)
    Savolainen, Jouni (Contributor)
    Kangas, Petteri (Contributor)
    Helaakoski, Heli (Contributor)
    Virkkunen, Riikka (Contributor)
    Muhos, Matti (Contributor)
    Taipale-Erävala, Kyllikki (Contributor)
    Parida, Vinit (Contributor)
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Sjödin, David (Contributor)
    Luleå University of Technology, Centre for Management of Innovation and Technology in Process Industry, Promote. Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    European roadmap for industrial process automation2018Report (Other academic)
    Abstract [en]

    This is an updated version of the ProcessIT. EU roadmap for industrial process automation, which was initially released in 2013 to provide guidance and input for process industry companies, providers of process industrial IT- and automation solutions, researchers as well as policy makers and bodies/initiatives that craft calls for RDI-projects. The main objective is for European process industry to stay competitive, profitable and sustainable. Thus, to support European process industry in its industrial process automation endeavours, the ProcessIT. EU roadmap outlines three top-level needs: sustainable production, competence management and trust, security, safety and privacy. These three top-level needs intersect the following ten R&D areas:

    • Productivity, efficiency, scalability and flexibility

    • Sustainability through circular economy - circular economy through industrial internet

    • Distributed production/modular factories and services

    • Artificial Intelligence and Big Data

    • Autonomous plants and remote operations

    • Platform economy

    • Cybersecurity

    • Safety - human, machine and environment

    • Competences and quality of work

    • Human-Machine Interfaces and Machine- to-Machine communications,

    which in turn are used as building blocks in the nine gamechangers . The gamechangers aim to influence the process industries’ competitiveness, profitability and sustainability . The gamechangers are listed below:

    • Modular factory for distributed and automated production

    • Live virtual twins of raw-materials, process and products

    • Increased information transparency between field and ERP

    • Real-time data analytics

    • Dynamic control and optimisation of output tolerances

    • Process industry as an integrated and agile part of the energy system

    • Management of critical knowledge

    • Semi-autonomous automation engineering

    • Integrated operational and cybersecurity management

    Finally, the ProcessIT. EU roadmap provides an insight into what may need to be considered on strategic and tactical levels, in terms of: objec-tives, R&D areas, game changers and business modelling, to keep and develop the competitive edge and initiative.

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