Sustainable Management of Physical Assets

Dear Colleagues,

Nowadays, physical assets are finally assuming the importance that they have always had, though not considered so by organizations over the past decades.

In fact, many companies, probably the majority, depend on physical assets, and the final value of products and services have a significant incorporation in the value of physical assets.

Because of this, the life cycle analysis of physical assets becomes a strategic subject that organizations must evaluate regularly.

Physical assets also must be analysed carefully, aiming to guarantee or improve their reliability. This is because condition-based maintenance, predictive maintenance and dynamic modelling is particularly important. Other tools, like fault trees, Markov chains, hidden Markov chains, and petri nets, among others, are important knowledge pieces that must be used in order to evaluate and improve physical asset’s reliability.

Based on the preceding, it can be concluded that it is necessary to have an excellent organization and management of physical assets, in conjunction with the best maintenance policies, to reach the maximum physical asset availability, in order to achieve their maximum productivity.

Additionally, the advent of the Standards ISO 55009, 55001 and 55002, among others, has boosted the preceding aspects.

But this new vision of physical assets must be considered from an environmental and sustainable perspective, both in what these concepts mean themselves and also how they are driven by the new standards that support them.

This Special Issue would like to encourage original contributions regarding the preceding aspects, though not necessarily limited to them.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

• Life cycle assessment;
• Standardization and certification;
• Predictive maintenance;
• Models of artificial intelligence;
• Data architecture and governance;
• Dynamic modelling;
• Sensing;
• Energy;
• Metrology;
• Risk.

We look forward to receiving your contributions.

Prof. Dr. José Manuel Torres Farinha
Dr. Hugo David Nogueira Raposo
Guest Editors

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• life cycle analysis
• sustainability
• environment
• predictive maintenance
• industrial maintenance
• condition based maintenance
• artificial intelligence
• energy
• metrology
• risk