Information for special stakeholders

You are a stakeholder interested in more information about our European research project IMPROVE? Please find detailed information about our major solutions specifically addressing the need of our stakeholders in the field of IT, Science, Manufacturing, Producing and Engineering.

  • Manufacturers
    Icon manufacturers

    Facing increased global competition, the manufacturing industry needs to reduce system downtime and component breakdowns to ensure resource efficiency and profitability. Aiming to provide the industry with new solutions and help boost productivity, the European research project IMPROVE has developed new virtual tools combining simulation with forecasting algorithms.

    The key achievements in the fields of simulation & optimization, condition monitoring, and alarm management can be implemented in new lines or as an upgrade to already existing production plants.

    IMPROVE’s solutions:

    • Provide the first simulation-optimization round trip solution ready for application
    • Supply manufacturers with innovative self-learning models for alarm management and condition monitoring
    • Provide a decision support system (DSS) visualising results and assisting the operator in taking the right choices in the manufacturing process
    • Benefits of our simulation & optimization tool:
      • Calculation of future investment
      • Reduce energy consumption and increase sustainability and efficiency
    • Benefits of our condition monitoring solution:
      • Provide a visual feedback to machine operators and maintenance teams regarding the health status of critical components to plan a replacement only when really necessary
    • Benefits of our alarm management solution:
      • Solution detects the “root cause” of an alarm flood and helps the operator solving it with a data-driven similarity learning, offline case-base construction, and case-based-reasoning (CBR)
    • Develop knowledge acquisition methods to translate implicit knowledge into explicit models of the machines represented by so-called cause and effect graphs and include it into data mining for efficient feature selection

    Target groups for our solution are packaging and automatic machine manufacturer as well as automation providers. Our tools are especially made for:

    • Industry using different machine types (filling machines, FFS (form, fill, and seal) machines, cartoning machines, palletizing machines, labelling machines, wrapping machines, and cleaning & sterilizing machines)
    • End-user industry (food & beverage, pharmaceuticals, chemicals, cosmetics, and others)

    Download Flyer Manufacturers

  • Producers
    Icon producers

    In the face of international competition and globalised production chains, new technologies are required to increase the productivity and efficiency in production plants. The European research project IMPROVE has developed innovative data-based solutions to enhance the productivity, reduce downtimes and increase the reliability of the production.

    Main achievements of the IMPROVE project are solutions in the fields of simulation & optimization, condition monitoring, and alarm management. These tools can be implemented as an upgrade to already existing production plants or in new lines.

    IMPROVE’s solutions:

    • Provide a simulation-optimization round trip solution ready to be implemented in industrial environment:
      • First tool combining simulation and optimization techniques on the market
      • First tool educating operators with augmented reality (AR) experience in the fields of process/machine KPI, machine documentation, and instructional content with video/audio
    • Provide an innovative self-learning condition monitoring solution that prevents producers from unexpected breakdowns or product degradation:
      • Realising data-driven condition monitoring: models are learned from data and are then used to detect and localise anomalies within the versatile production system
      • Carries out short-time forecast analyses to identify wrong machine settings after a product changeover or problems related to raw material changes
    • Provide the first alarm management algorithm based on case-based-reasoning (CBR) and data-driven similarity learning that integrates expert knowledge:
      • Combining similarity measure learning, offline case-base construction, semi-supervised learning, online flood detection, and CBR
      • Suggesting solutions in case of alarm floods (identification of the flood, repair instructions, etc.)
    • Provide a decision support system (DSS) visualising results and assisting the operator to take the right choices in the manufacturing process
    • Develop knowledge acquisition methods to translate implicit knowledge into explicit models of the machines represented by so-called cause and effect graphs and include it into data mining for efficient feature selection

    Your benefits:

    • Enhance productivity
    • Reduce downtime
    • Cut down costs
    • Reduce employee stress
    • Boost production efficiency
    • Increase profit
    • Download Flyer Producers

  • Scientists
    Icon scientists

    IMPROVE brings together leading scientists in the fields of automation technologies and IT in Europe and beyond. Together, we work on ground-breaking solutions related to simulation & optimization, condition monitoring, and alarm management to pave the way for new industrial technologies. Solutions within the project have been implemented in demonstrators and already been tested in industry settings.

    IMPROVE’s key findings:

    • Development of a novel tool combining simulation & optimization techniques:
      • Automating tasks in modelling development to reduce resources
      • Tool configures the simulation model automatically based on a model library
    • Development of an underlying machine learning framework used for condition monitoring:
      • Enhancement of existing machine learning methods for application in industrial machines/CPPS
      • Providing a learning framework with flexible data acquisition applicable in modern technologies such as OPC UA, but also as a retrofit in existing industrial plants
    • Development of an algorithm in the field of alarm management:
      • First algorithm based on case-based-reasoning (CBR) and data-driven similarity learning integrating expert knowledge
      • Combining similarity measure learning, offline case-base construction, semi-supervised learning, online flood detection, and CBR
    • Provide a decision support system (DSS) visualising results and assisting the operator to take the right choices in the manufacturing process
    • Develop knowledge acquisition methods to translate implicit knowledge into explicit models of the machines represented by so-called cause and effect graphs and include it into data mining for efficient feature selection

    Download Flyer Scientists

  • Engineers
    Icon engineers

    In today’s competitive, global industrial environment, engineers rely on high-level solutions for machines in industrial production. The European research project IMPROVE has developed innovative data-based tools to enhance efficiency and productivity in the fields of simulation & optimization, condition monitoring, and alarm management.

    IMPROVE’s solutions:

    • Provide a simulation-optimization round trip solution ready to be implemented in industrial environment:
      • First tool combining simulation and optimization techniques on the market
      • First tool educating operators with augmented reality (AR) experience in the fields of process/machine KPI, machine documentation, and instructional content with video/audio
    • Provide an innovative self-learning condition monitoring solution that prevents producers from unexpected breakdowns or product degradation:
      • Realising data-driven condition monitoring: models are learned from data and are then used to detect and localise anomalies within the versatile production system
      • Possibility of retrofitting of old machines as data acquisition for learning is flexible
      • New machine design can directly incorporate technologies for condition monitoring
    • Provide the first alarm management algorithm based on case-based-reasoning (CBR) and data-driven similarity learning that integrates expert knowledge:
      • Combining similarity measure learning, offline case-base construction, semi-supervised learning, online flood detection, and CBR
      • Supporting the operator to handle an alarm flood:
        • System gives a suggestion “where to look first”
        • Good support for unexperienced operators
    • Provide a decision support system (DSS) visualising results and assisting the operator to take the right choices in the manufacturing process
    • Develop knowledge acquisition methods to translate implicit knowledge into explicit models of the machines represented by so-called cause and effect graphs and include it into data mining for efficient feature selection

    Download Flyer Engineers

  • IT
    Icon IT

    Industry 4.0 is a growing market and new solutions are required to face the global competition in production chains. Within the IMPROVE project, 13 partners from software development, industry, and academia have developed new data-based solutions in the fields of simulation & optimization, condition monitoring, and alarm management to boost the efficiency in production plants.

    IMPROVE’s solutions:

    • Provide a simulation-optimization round trip solution:
      • First tool combining simulation and optimization techniques on the market
      • First tool educating operators with augmented reality (AR) experience in the fields of process/machine KPI, machine documentation, instructional content with video/audio
    • Provide an innovative self-learning condition monitoring solution that prevents producers from unexpected breakdowns or product degradation:
      • Realising data-driven condition monitoring: models are learned from data and are then used to detect and localise anomalies within the versatile production system
      • Different software options are based on the self-learning tool and can be implemented in various industrial surroundings as condition monitoring knowledge can be combined with existing management systems such as Zenon, TT Enterprise Data Server, and SAP
      • Condition monitoring software can be adjusted to manufacturers' and producers' needs
    • Provide the first alarm management algorithm based on case-based-reasoning (CBR) and data-driven similarity learning that integrates expert knowledge:
      • Combining similarity measure learning, offline case-base construction, semi-supervised learning, online flood detection, and CBR
      • Suggesting solutions in case an alarm flood occurs (identification of the flood, repair instructions, etc.)
      • Algorithm can be used by industrial software developer and be added to existing software or to a data management platform
    • Provide a decision support system (DSS) visualising results and assisting the operator to take the right choices in the manufacturing process
    • Develop knowledge acquisition methods to translate implicit knowledge into explicit models of the machines represented by so-called cause and effect graphs and include it into data mining for efficient feature selection

    Download Flyer IT