PROSAM

Intelligent Fault Prognosis Systems for Anticipative Maintenance Strategies

The research project „PROSAM“ has started in December 2014 within the scope of a funding framework of the Austrian Research Promotion Agency (FFG), with the aim to research the basics of anticipatory maintenance approaches and provide the industry with suitable solutions. The members of the consortium were selected in order to guarantee that all relevant competences are covered: data integration and both knowledge and data-based modelling (SCCH), expertise in maintenance management (H&H Systems GmbH), and condition monitoring services (Messfeld GmbH).

Goal

The overall aim of this research project is to develop fault prediction models that are enhanced by combining expert knowledge and data based approaches (based on methods from computational intelligence) and that then form a basis for anticipatory maintenance management strategies. This is a requirement to reach the economic goal of increasing operational availability and reduction of costs (in terms of human and physical resources).

Challenges

Despite the huge optimization potential, anticipative maintenance strategies are only rarely used in industrial applications. Reasons for this can be found in the ever increasing complexity of production plants and the increasing diversity of components. This system complexity poses one of the major challenges in the development of a strategic maintenance management.

Approach

The project PROSAM meets this challenge and intends to deal with this problem by developing novel methodical and methodological approaches. The combination of data driven models with knowledge based methods and their optimal integration into the planning of a strategic maintenance management are seen as the key factors to success. The approaches should incorporate all crucial aspects such as data integration, data processing, extraction of features, model building, knowledge representation and problem oriented system analysis.

Benefit

  1. Increase of plant availability, since an early identification of faults can avoid or at least reduce downtimes.
  2. Reduction of material and energy costs, as maintenance activities are no longer carried out on pre-defined schedules but only when necessary. A premature replacement of functioning components is thus avoided.
  3. Easier planning and reduction in complexity of maintenance procedures due to a continuous condition monitoring.
  4. Increase of operational safety due to the prevention of damages, which may have severe consequences for human beings and the environment.

Project Data

Duration: 01.12.2014 - 30.11.2017
Budget: ca. 574.000 € total costs and ca. 459.000 € funding contribution
Partners: Software Competence Center Hagenberg, H&H Systems Software GmbH, Messfeld GmbH
Funding partner: FFG, ICT of the Future - 2nd Call

Contact

Bernhard Freudenthaler

Freudenthaler Bernhard

Area Manager Data Science
Phone: +43 50 343 850

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