Augmented Reality in Maintenance: An information-centred design framework

Augmented Reality (AR) visualization capabilities can impact on maintenance. From enhancing performance to retrieving feedback, AR can close the information loop between maintenance information systems and the operations supported. Though, the design of AR applications is not aligned with current information systems, which prevents maintenance information to be used and improved properly. In this paper, industrial collaboration contributed to determine a framework for AR integration in maintenance systems. The framework describes information types, formats and interactions modes for AR to enhance efficiency improvements in maintenance of complex equipment. Semi-structured interviews and surveys with maintainers were conducted to determine the maintenance challenges and also to validate the framework proposed. Therefore, exposing future research in topics such as multimodal interaction, information contextualization and performance analysis to achieve the complete integration of AR in maintenance

A systematic review of Augmented Reality content-related techniques for knowledge transfer in maintenance applications

Augmented Reality (AR) has experienced an increasing trend in applied research in the last few years. This emerging trend is focused in content-related challenges: mainly creation (Authoring), adaptation (Context-Awareness) and improvement (Interaction-Analysis) of augmented content. Research in these techniques has enabled Academia to recognise Augmented Reality capability for knowledge transfer, either from AR systems to users or between users. But to the best of author’s knowledge, there are no specific literature review in these areas, neither on their relations with AR knowledge transfer ability. Therefore, this paper aims to identify these relations through an analysis of state-of-the-art techniques in Authoring (A), Context-Awareness (CA) and Interaction-Analysis (IA) in the context of maintenance applications. In order to do so, a Systematic Literature Review (SLR) has been conducted on 74 application-relevant papers from 2012 to 2017. It comprised a thematic analysis to establish the relation between maintenance applications, research in A, CA and IA and AR knowledge transfer modes. Its results helped to classify AR maintenance-applications by technological readiness levels. They also revealed the potential of AR for users’ knowledge capture, and future research required for full knowledge management capabilities. Furthermore, the SLR method proposed could be extended to correlate AR systems and applications by their knowledge management capabilities in any AR application context

Structured authoring for AR-based communication to enhance efficiency in remote diagnosis for complex equipment

Remote diagnosis procedures are prone to communication errors due to varying levels of experience and knowledge between expert maintainers and technicians. These result in inefficiencies that delay the diagnosis process. The aim of the paper is to develop a Structured-Message Authoring framework for Augmented Reality (AR) Remote Communication (SMAARRC) and to evaluate its ability to enhance the efficiency of remote diagnosis services. The framework proposes a message structure and automatic AR content creation rules for it that enable data capture and sharing within a remote context. Laboratory experiments present an average time reduction of 56% for remote calls while maintaining same quality compared to traditional remote communication methods (phone calls and emails). Remote experts feedback evidence the usability and feasibility of this framework to work in real-life conditions

Developing an ontological framework for effective data quality assessment and knowledge modelling

Big data has become a major challenge in the 21st century, with research being carried out to classify, mine and extract knowledge from data obtained from disparate sources. Abundant data sources with non-standard structures complicate even more the arduous process of data integration. Currently, the major requirement is to understand the data available and detect data quality issues, with research being conducted to establish data quality assessment methods. Further, the focus is to improve data quality and maturity so that early onset of problems can be predicted and handled effectively. However, the literature highlights that comprehensive analysis, and research of data quality standards and assessment methods are still lacking. To handle these challenges, this paper presents a structured framework to standardise the process of assessing the quality of data and modelling the knowledge obtained from such an assessment by implementing an ontology. The main steps of the framework are: (i) identify user’s requirements; (ii) measure the quality of data considering data quality issues, dimensions and their metrics, and visualise this information into a data quality assessment (DQA) report; and (iii) capture the knowledge from the DQA report using an ontology that models the DQA insights in a standard reusable way. Following the proposed framework, an Excel-based tool to measure the quality of data and identify emerging issues is developed. An ontology, created in Protégé, provides a standard structure to model the data quality insights obtained from the assessment, while it is frequently updated to enrich captured knowledge, reducing time and costs for future projects. An industrial case study in the context of Through life Engineering Services, using operational data of high value engineering assets, is employed to validate the proposed ontological framework and tool; the results show a well-structured guide that can effectively assess data quality and model knowledge

Augmented Reality for Remote Assistance

Augmented Reality (AR) reduces the technicians’ cognitive effort mainly resulting in both time and error rate reductions. Still, its application in remote assistance has not been fully explored yet. This paper focuses on understanding the benefits of providing assistance to a remote technician through AR. Augmented Reality for Remote Assistance (ARRA) has been designed and developed for local novice maintainer to request assistance and communicate with a remote expert. The remote expert can manipulate virtual objects, which are then overlaid on the real environment of the novice maintainer. ARRA has been tested with the help of 60 participants. This involved performing an assembly/disassembly operation on a mock-up of a piping system. The participants were remotely assisted through ARRA or video-call. Quantitative spatial referencing error data has been collected. The results showed a 30% improvement in terms of spatial referencing when utilising ARRA as remote assistance support as opposed to video-call. Future studies should investigate into quantifying the improvements due to other factors involved in remote assistance, especially language barriers and connectivity issues