Ian D. Peake

Jan Olaf Blech, Ian D. Peake, Sudarsan SD

Means to support collaboration for remote industrial facilities such as mining are an important topic, especially in Australia, where major mining sites can be more than a thousand kilometers from population centres. Software-based collaboration and maintenance solutions can help to reduce costs associated with these remote facilities. In this paper, we report on our collaborative engineering project providing a decision support solution tailored for Australian needs. We present two application examples: one related to incident handling in industrial automation, the other one in the area of smart energy systems.

Ian D. Peake, Jan Olaf Blech, Shyam Nath et al.

In this report, we present our work in visualization and augmented reality technologies supporting collaboration in manufacturing automation. Our approach is based on (i) analysis based on spatial models of automation environments, (ii) next-generation controllers based on single board computers, (iii) cloud-, service- and web-based technologies and (iv) an emphasis on experimental development using real automation equipment. The contribution of this paper is the documentation of two new demonstrators, one for distributed viewing of 3D scans of factory environments, and another for real time augmented reality display of the status of a manufacturing plant, each based on technologies under development in our lab and in particular applied to a mini-factory.

Ian D. Peake, Jan Olaf Blech, Lasith Fernando et al.

This paper describes a method for the recovering of software architectures from a set of similar (but unrelated) software products in binary form. One intention is to drive refactoring into software product lines and combine architecture recovery with run time binary analysis and existing clustering methods. Using our runtime binary analysis, we create graphs that capture the dependencies between different software parts. These are clustered into smaller component graphs, that group software parts with high interactions into larger entities. The component graphs serve as a basis for further software product line work. In this paper, we concentrate on the analysis part of the method and the graph clustering. We apply the graph clustering method to a real application in the context of automation / robot configuration software tools.