The paper aims to investigate the reasons why Augmented Reality (AR) has not fully broken the industrial market yet, or found a wider application in industries. The main research question the paper tries to answer is: what are the factors (and to what extent) that are limiting AR? Firstly, a reflection on the state of art of AR applications in industries is proposed, to discover the sectors more commonly chosen for deploying the technology so far. Later, based on a survey conducted after that, three AR applications have been tested on manufacturing, automotive, and railway sectors, and the paper pinpoints key aspects that are conditioning its embedding in the daily working life. In order to compare whether the perception of employees from railway, automotive, and manufacturing sectors differs significantly, a one-way analysis of variance (ANOVA) has been used. Later, suggestions are formulated in order to improve these aspects in the industry world. Finally, the paper indicates the main conclusions, highlighting possible future researches to start.

Smart operations require the ability to generate alternative plans whenever a change in operations occurs in an unplanned manner. Alternate maintenance plans, in a highly dynamic context such as airline operations, require the ability to foresee small developments in terms of labor allocation, repairable items, and downtime, when and where they were not previously scheduled. In addition to being able to cause the disruption of the air transport network and consequent financial losses, it causes loss of trust in the company brand. Prescriptive maintenance is a potential technological response when using Artificial Intelligence to suggest alternative plans promptly so that decisionmakers can reduce the impact on air operations. This paper proposes a framework for the construction of an integrated prescriptive maintenance solution that is certifiable by using auditable methods and extensible to complex systems of other industries. The adoption of prescriptive maintenance not only enhances the use of health management systems, widely available in modern aircraft fleets that have the potential to predict the remaining useful life of items of interest, but also allows identifying more than one response alternative to conflicts of interest in the conduction of the smart operations of air transport companies.

PurposeA framework is being developed to help Integrated Electronic Technical Publications (IETP) consultation inside and outside the aviation maintenance hangar. The expected results are the reduction in time to access the desired IETP and to assist mechanics while performing maintenance tasks using voice recognition.Design/methodology/approachThe work is being conducted based on literature review and consultation with mechanics from the aviation industry, through questionnaires. The development will be made through study cases by building a core search engine and mobile applications to support the mechanics during the maintenance activities.FindingsThe identified problem in small maintenance shops and defence organizations suggests that IETP are not entirely accessible before and during the maintenance activity. Such organizations suffer from information and communications technology (ICT) low infrastructure capability and demand access to multiple IETP databases as they usually support different aircraft. To have access to the IETP through voice assistant application will help mechanics to access the IETP, including when they would be with dirty hands and having difficulty in using mobile devices with touch displays.Originality/valueThe framework being developed will give mechanics the ability to quickly find any existing IETP to support its maintenance task at any time and in any place with low demanding for ICT infrastructure. The architecture will support different applications, and the identified priority is for IETP viewers to the most demanding functionality of specification ASD S1000D. This approach could also help in troubleshooting activities since COVID-19 brought new demands for the social distancing for mechanics.

Aerospace systems are composed of hundreds or thousands of components and complex subsystems which need an appropriate health monitoring capability to enable safe operation in various conditions. In terms of monitoring systems, it is possible to find a considerable number of state-of-the-art works in the literature related to ad-hoc solutions. Still, it is challenging to reuse them even with subtle differences in analogous subsystems or components. This paper proposes the Generic Anomaly Detection Hybridization Algorithm (GADHA) aiming to build a more reusable algorithm to support anomaly detection. The solution consists of analyzing different supervised machine learning classification algorithms combined in ensemble techniques, with a physical model when available, and two levels of a decision to estimate the current state of the monitored system. Finally, the proposed algorithm assures at least equal, or, more typically, better, overall accuracy in fault detection and isolation than the application of such algorithms alone, through few adaptations.