Project Objectives
1. sensors
Interfacing with conventional sensors and ultra speed cameras to collect and process production equipment data from the field, in order to feed real-time prediction and failure detection models.
2. machine learning
Design of machine learning models that accurately predict the failure timeline and the estimated remaining equipment life, detecting current or evolving failures.
3. Risk and failure management
Risk and failure management according to IEC60812 standard, by analyzing their occurrence mechanism and determining their criticality and impact.
4. DSS
Automated decision support (DSS) to assess equipment performance and accurately predict and diagnose failures and fatigue. It will be combined with innovative strategies to predict, diagnose, prevent, manage, remediate and synchronize.
5. ERP and MES
Interfacing with Enterprise Resource Planning (ERP) and Manufacturing Execution Systems (MES) for optimal synchronization of maintenance work with production requirements and planning.
6. Case Study
Evaluation (a) of effectiveness and reliability, (b) user acceptance and (c) impact of the integrated PREDICT system in the business environment of 2 industries: Loulis Mills (the largest grinding company in the Balkans, listed in Stock Market) and KEBE (the largest and most modern ceramics factory in Europe).
7. Design
Creation of a Business Plan and a plan for International Commercialisation, design and implementation of strategies for managing the produced innovation.
8. Patent
Actions to support and boost the produced innovation, including preparation for the submission of at least one international patent.
9. Dissemination and communication
Dissemination and communication of PREDICT results to the international scientific and business community.
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