The objective of this thesis is to develop a maintenance approach that fulfills the requirements of Industry 4.0. It explores the role and importance of maintenance activities in today’s industry. Then, it develops the features and tasks required to be performed by maintenance to fulfill the demands of Industry 4.0. Finally, it develops a reference model to be used in designing maintenance system for Industry 4.0. To perform these studies, real data were collected and applied as well as a typical scenario was implemented.

The results achieved in the papers of this thesis are 1) a mathematical representation and application of a model that identifies, analyses and prioritizes economic weakness in working areas related to production, 2) a model that analyses, identifies and prioritizes failures that impact the competitive advantages and profitability of companies, 3) characterization of a suitable maintenance technique for Industry 4.0 and 4) a reference model i.e. a framework, that could be utilized to develop a maintenance approach for Industry 4.0.

The conclusion of this thesis confirms that maintenance has a significant impact on companies’ competitive advantages, other working areas and profitability. To achieve a suitable maintenance technique for Industry 4.0, this technique must be able to monitor, diagnose, prognosis, schedule, assist in execution and present the relevant information. In order to perform these tasks several features must be acquired, the most important features are to be: digitized, automated, intelligent, able to communicate with other systems for data gathering and monitoring, openness, detect deviation in the condition at an early stage, cost- effective, flexible for adding new CM techniques, provide accurate decisions and scalable. The developed framework could be used as a base to design a maintenance system for Industry 4.0. This study contributes to our understanding of the maintenance importance in today’s industry and how to develop a maintenance approach for Industry 4.0.

Over time, maintenance methods have developed following the dynamic manufacturers’ demands. Now, with the coming industrial revolution, new maintenance approaches have to be developed to fulfil the new demands of future industry, as well as to allow companies to benefit from technological advances. Therefore, the research question of this study is: how to develop a maintenance approach for factories of the future? To answer this question, this thesis proposes tools to identify and prioritise maintenance related problems that impact company’s profitability. It explores designing and implementation of a digitalised maintenance approach for future factories. Furthermore, it investigates tools and methods to collect data efficiently by sensors.

The results achieved in this thesis are 1) a mathematical representation and application of a model that identifies and prioritises causes of deficiencies in production processes, 2) a model that identifies and prioritises failures that impact the competitive advantages and profitability of companies, 3) characterisation of a maintenance approach for future factories, 4) frameworks that could be utilised to develop a maintenance approach for future factories, as well as, guidelines that help to design this approach, 5) guidelines for the integration of digitalised maintenance with the database of other working areas, 6) an algorithm for adaptive sampling for sensors, as well as, a proposal for a generic software architecture to facilitate designing, modelling and implementation of adaptive sampling algorithms.

The conclusion of this thesis confirms previous findings that maintenance has an impact on companies’ competitive advantages, other working areas and profitability. To design and implement a maintenance system, its elements should be extracted from the primary objective of maintenance. These elements should be then allocated in a suitable architecture and their mechanism should also be defined. Prior to implementation and integration, mapping the concept design to production problems can be used to examine its performance. An approach to collect data efficiently by sensors is to use adaptive sampling. The developed adaptive algorithm and the reference software framework for adaptive sampling algorithms could be used for this purpose.