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Maintenance models for zero-unexpected-breakdowns

Strategies and predictive maintenance models wrapped around physical systems for zero-unexpected-breakdowns and increased operating life of factories (Z-BRE4K)

Maintenance in general, and predictive maintenance strategies in particular, face significant challenges with the evolution of the equipment, instrumentation and manufacturing processes they support. Preventive maintenance strategies, designed for traditional highly repetitive and stable mass production processes based on predefined components and machine behaviour models, are no longer valid and more predictive-prescriptive maintenance strategies are needed.

Z-Bre4k impact to the European manufacturing industry and the society can be summarised in the following: (i) increase of the in-service efficiency by 24%, (ii) reduced accidents, (iii) increased verification of objectives, (iv) 400 new jobs created and (v) over €42M ROI for the consortium.

To do that we have brought together a total of seventeen EU-based partners, representing both industry and academia, with ample experience in cutting-edge technologies and active presence in the EU manufacturing.


Meet the Principal Investigator(s) for the project

Dr Alireza Mousavi

Related Research Group(s)

matrix

Intelligent Data Analysis - Concerned with effective analysis of data involving artificial intelligence, dynamic systems, image and signal processing, optimisation, pattern recognition, statistics and visualisation.

machine digital (3)

Digital Manufacturing - Being at the forefront of solutions for building smart machines, we create an operational framework for the digital transformation to Industry 4.0.


Partnering with confidence

Organisations interested in our research can partner with us with confidence backed by an external and independent benchmark: The Knowledge Exchange Framework. Read more.


Project last modified 03/12/2021