| dc.description.abstract | Facility management is a developing discipline that has received attention from both professionals
and researchers in recent years. In industry, this is mainly due to the importance of efficiency in the
production process and to its economic relevance.
Modern facility management considers various interests related to material resources, and among
others, social and environmental interests. An important opportunity for the improvement of this
discipline derives from the introduction of industry 4.0 technologies for the management of material
resources.
The goal of this research is to develop a general approach for maintenance management of industrial
facilities based on Industry 4.0 technologies, to support decision-making in maintenance schedules
and contribute to the continuous improvement of maintenance activities, from which also derives the
improvement of the production process performance.
Starting from a facility management model for the maintenance of industrial assets, we develop a
general approach to maintenance based on the Internet of Things and Cyber-Physical Systems, which
allows us to reason about the implementation of an effective Organisational Facility Management
Unit. Then, leveraging on the Internet of Things, Big Data and Machine Learning technologies for
acquiring, analyzing, and processing industrial data, we contribute to the improvement of industrial
facilities management by delivering a new methodology that has allowed the design and
implementation of new tools to support the management of industrial facilities.
In particular, we will focus in this work on the problem of machine tool maintenance and propose
two new software tools that take advantage of Industry 4.0 technologies to improve the traditional
approaches proposed in the Total Productive Maintenance area.
The first tool is a software application developed to support the processes of planning and execution
of maintenance operations, maximizing the effectiveness of the maintenance management strategies
Time-Based Maintenance and Breakdown Maintenance. The second tool is a Predictive Maintenance
application developed to support decision-making processes in maintenance schedules, using the
Gaussian mixtures technique. The predictive model has been applied to real data from the Italian
automotive manufacturing industry.
This study proposes a methodology that can be used as a guideline for the implementation of a facility
maintenance office that pursues continuous improvement in the management of industrial assets
within the scenario of Industry 4.0. [edited by Author] | it_IT |
