Exit Menu

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highly repetitive and stable mass production processes based on predefined components and machine behaviour models, are no longer valid and more predictive-prescriptive maintenance strategies are needed.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 - Graduated as BEng with equivalent of first class from Tehran Azad University in Industrial Engineering, Planning and Analysis of Systems in 1994. I worked first as placement and then full time in Automotive Industry Management Consultancy from 1992-1996. In 1996 I joined the Postgraduate Research programme (PhD) of the well-known Department of Manufacturing and Engineering Systems of Brunel University with a scholarship from the University. I obtained my PhD in May 2000. In year October 1999, I was appointed as an RA on an EPSRC/MAFF project MEATRAC – where I developed a fully novel monitoring and control system using Sensors & Actuation, SCADA, PLC, RFID Technology, and Enterprise Data Management System for 100% Tracking and Traceability of Meat products. It was successfully delivered in mid-2002. From May 2002, I was appointed as a lecturer in the same department and to date have covered a wide range of teaching and supervising UG and PG projects in subject areas ranging from mathematics, software engineering, software development, systems modelling & probability theory, control, and embedded systems. The modules covered all undergraduate and postgraduate years, and taught in a highly international and diverse cohort of students. I contribute to a wide range of classical (e.g. mathematics, probability theory, queuing theory, discrete systems, software development) and modern subjects (e.g. Machine Learning, AI, Applied Control, and Cyberphyisical systems) at the Departments of Computer Science, Electronic and Copter Engineering as well as Department of Mechanical & Aerospace Engineering with the College of Engineering, Design and Physical Sciences. My current research activities are concentrated on digital transformation and smartification of Industrial Systems, especially within the Industry 4.0 context covering sensors-actuation, signal processing and feature extraction, machine learning, modelling, control and optimisation. For complete list of publications and other information please visit my website: http://www.brunel.ac.uk/~emstaam/ Systems Engineering REsearch Group (SERG) Website: http://www.brunel.ac.uk/~emstaam/SERG_BRUNEL/index.html University site about SERG (https://www.brunel.ac.uk/computer-science/research-and-phd-programmes/System-Engineering-Research-Group)  Special Announcement: SERG is Recruiting Research Assistants, Fellows and PhD students in the areas of Automation, Control, Sensors and Actuation, Mathematical Modelling and Optimisation, Machine Learning & AI, and Software Engineering (contact me for details)

Related Research Group(s)

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.


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

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