Dr Dedy Ariansyiah: Hi everyone welcome to this webinar. In this webinar i'm going to talk about the potential of ar technology to improve remote communication in maintenance. My name is Dedy Ariansyiah and i'm a research fellow in ar for true life engineering for Cranfield University.

Some points that i will cover in this talk are the research background and the problem of remote communication in maintenance and then i will give an overview of how ar technology has been implemented to support remote maintenance.

After that i will talk about our work in developing a base remote communication framework to improve remote diagnosis this will be followed by how we validated our approach by comparing ar versus no ar solution. Finally some conclusion and further research in this area will be given in the last presentation.

The ultimate goal of maintenance manager in any industrial sectors like aviation manufacturing and railway is to maximize the uptime of the production asset and to keep the downtime minimum.

Every physical asset is subject to a degradation and will eventually fail at a certain point of time. Although the remaining useful life of an asset can be predicted thanks to iot sensor and predictive models still the maintenance quality and its associated costs are highly dependent on the skill set of a technician who carries out the task.

Given that a skilled technician is not always available on site when the failure occurs the question that we are trying to address is how could we effectively and efficiently provide access to the expert knowledge when it is needed.

The conventional method is to use telephone and email to reach to a skilled technician however this approach leads to a long waiting time in communication and often misunderstanding which result in loss in production time and inefficiency use of resources. At Cranfield University we developed ar-based solution that offers a novel way of visualizing and exchanging information between a remote expert and a local technician.

The intention of develop approach is to improve communication and therefore reduce errors and misunderstanding as well as time-consuming tasks. In the academic literature we found 20 papers from 2010 to 2018 that address the application of ar for remote maintenance. This paper can be categorized into four main areas, the majority of the study was focused around process guidance followed by training remote assistance and data collection.

A further analysis into this literature we identified at least three main areas that require further research. First is structure communication which refers to a way remote communication is regulated to reduce ambiguity.

Second is automatic data collection which refers to an automated mechanism for maintenance data gathering to improve maintenance communication during the collaboration task. The third is an ex system which refers to a recommended system that can advise a technician in troubleshooting a problem in unknown situation. This talk will focus on how ar-based framework maintenance especially for diagnosis tasks can be enhanced using structure communication.

In order to address the question the approach that we had taken was to develop a base remote communication framework which was built on two main components. First an innovative message structure and then and then second is a rule-based authoring approach for automatic ar content creation. Looking at the left figure we can see the conventional remote collaboration make use of telephone and email to deliver unstructured message and other related information.

With this approach it is difficult to ensure that a message is transparent or easily understood because of a knowledge gap that might exist between a remote expert and a local technician. In contrast our approach uses structure message and a rule-based authoring that decompose message into information elements and transform them into ar instructions that can enhance remote communication for diagnosis tasks.

This is the example of message structure and ar visualization for both expert and technician view. At the bottom you can see message elements of the message structure. The message structure was developed using the 5w use method.

Using this message element it allows us to address three main challenges. First is the ability to construct an effective message that can describe any procedures and second is the ability to record and to replay a call based on the message box and third is the ability to automatically create ar instructions.

The second element of the framework is the development of a rule-based altering approach. In the left figure we can see an ar instructions altered by a remote expert can be overlaid in the technician field in the real environment.

Unlike the traditional approach, ar-based solution can provide two ways of awareness. First is the object awareness which refers to the identification of the object being referred.

Second is the procedure awareness which refers to the procedure to be performed on the object being referred and here comes the validation part our arb system.

Implementation consists of three elements, first is a pc used by an expert to create ai instructions, second is a cloud server that store the information and provide data access to a database, third is a technician order lens which is a device used by a technician to visualize ar information in the real environment.

Other lens is equipped with a web camera that can be used to live stream what the technician is looking at to the remote expert. We carry out experimental validation to evaluate if the develop approach can actually enhance remote diagnosis tasks in terms of errors completion time and the complexity of the message. There were three different independent variables and two evaluation which involves a total of 30 msc students for performance evaluation and eight industrial users for usability and feasibility evolution.

And here is a more detailed information of the experimental tasks that we asked our participants to carry out. There were four messages, from simple to complex message a refers to a remote expert asking a participant to unscrew the screw of the front panel of the field hatch and open it.

Message b refers to an expert asking a participant to visually inspect the left and the right side of the field hatch and take a photograph of every defect found. Message c refers to an expert asking a participant to repair any defect by placing a patch.

D refers to an expert asking a participant to search and take a photograph of the previous reparation result and send it by email. Here are the results that we have obtained from our validation test. In terms of errors we found that the number of testers who made mistake during remote diagnosis tests were similar across different experimental groups. This is also true for the total number of errors across experimental groups which implies that the accuracy of remote diagnosis using augmented reality remained unaffected. In terms of time, we found that the reduction time for remote diagnosis was achieved. Using augmented reality solution the average reduction time using ar was 56 percent in comparison to no ar solution so it leads to more than half time efficiency which entails to a better use of expert time.

Furthermore the reduction time was incremented as the complexity of the message increased. This implies that remote diagnosis using our ar solution can achieve more time saving as the complexity of the message increases. Finally we also got the result of usability and feasibility evaluation from the testers and the industrial users opinion from the data collected.

We found similar results between two user groups which shows some evidence that our ar solution for remote diagnosis is indeed useful and applicable in real life context. Therefore based on our validation results we can conclude that our aovs approach using message structure and allow.

This authoring can be used to improve the efficiency of remote diagnosis in terms of time reductions however the total number of errors were similar between our arbs approach and no ar in remote technologies.

Further we've also got the results that the experts feel were similar with the tesla fields in terms of usability utility and visibility to our ar solution. As part of the future work we should address the efficiency of arv solution on the side of the remote expert. Finally we should also address the potential use of structural communication for developing a recommender system.

Thank you for attending this webinar i'm happy to answer if there is any questions related to this talk?