A joint EPSRC-funded Impact Acceleration Account (IAA) project titled “Big-data: improving aircraft performance” has been funded to exploit the findings and methods we’ve developed in the Airbus In-Service department.
This six-month long project led by Dr Lei Shi and Prof. Linda Newnes aims to embed the approaches created within the project, including big-data analytics, trend analysis and autonomic computing, to interrogate and categorise aircraft wing In-Service projects. The research at the University of Bath has demonstrated that it is possible to automatically predict the complexity, duration and cost of such repair cases. This has been achieved through interrogating 10,000+ historical projects to create and validate the proposed approaches. Initial tests have been completed to ascertain whether the approaches can be used on the ‘live’ data from the Airbus In-Service workflow system.
Our overall aims are to develop the processes through on-site development and testing, to make the approaches self-sustaining, and to assist the in-service teams with their decision-making.
Do you face similar challenges? Let us know in the comments below…
Recently we’ve been continuing our work with Formula Student (FS) as we move forward towards developing an FS dashboard. The LOCM project has been working with project data (e.g. CAD files, reports, documents, communication) generated by FS teams in the development and refinement of data analytic approaches.
Over the past few weeks, we have been conducting interviews with student project managers and academic supervisors involved in FS across three universities to gain insights into key areas of project activity, goals and issues that could be supported by project dashboards. This work has enabled us to develop user-driven design scenarios and requirements, and importantly, will help guide how we apply the data analytics that have been developed in the creation of visualisations and dashboards that are both usable and add value to FS project activity.
We’ve had a busy couple of months as we begin to consolidate the various strands of work and ramp-up our industrial engagement. We ran two Participatory Design Sessions in May, picking the brains of over 50 engineers – one with Frazer Nash Consultancy, and one at the Design 2016 International design conference, with a great mix of industry and academic input…
Continue reading “Industrial engagement and interactive dashboards”
In this quarter we have demonstrated analytical approaches for revealing previously hidden product and process dependencies through analysis of User-CAD interaction and content of technical reports/communications and novel methods for monitoring and predicting likely project complexity for routine projects.
For example, we’ve been using co-occurrence analysis to reveal model product dependencies. However, unlike traditional methods, we can also include data from representations such as CAD models:
The major focus of our research effort has been on the acquisition of further datasets, preparation of prototype dashboards (vision demonstrators) and preparation of conference papers.
For example, here is a new composite of various analyses:
We’re also pleased to report that a total of five conference papers were accepted for presentation at the prestigious International Conference on Engineering Design (http://iced2015.org/), to be held in Milan in July. Details of these papers may be found in our publications section. We are also pleased to announce that RWA have now joined the project.
Following the Project Advisory Group meeting of our industrial partners in the summer, and further feedback from industrial partners, the focus of research has been on the following areas: the configuration of prototype project dashboards and the development of the concept of engineering project health monitoring, and in particular, the proxies of performance of engineering projects – i.e. features of interest for project stakeholders. To address the latter a series of ethnographic studies are to be undertaken.
Below are some examples of composites of various analyses we are now able to undertake. Here we are mapping sentiment and type of email being sent onto a representation of a product – who is saying what about each part of the product? This could give project managers valuable early warning about potential issues:
Similarly, this example dashboard shows various information about aircraft repairs, using a visual representation of the aircraft and damage location: