Mining Machine Fleet Management Data Analysis
Location: Kewdale, WA
Duration: 3-5 months
Proposed start date: ASAP
RCT-Global provides fleet management solutions that are designed to deliver relevant and accurate machine data that enables fleet users to make informed decisions when managing their fleet. The solutions are available for a large number of mining machine types and brands and will capture data to fully assess machine and operator performance in real time and presents intelligent reports with the view to improve productivity.
The data obtained from the machines ranges from utilisation hours, idle hours, vehicle speed, gear settings, driving time, dumping time to guidance information and machine performance such as fuel consumption, engine temperatures and many, many more data, which is collected at high rates from live mining machines at various sites across the world and then transported to the cloud for analysis.
The aim of this project is to value add to the acquired data so that RCT can provide additional productivity improvements to their customers on top of the information already provided. Transforming data into information for customers to enable them to make smart decisions is the goal of our work.
Research to be Conducted
As an intern at RCT you would be able to use your skills in data analysis to explore new methods and algorithms that can be applied to the collected data so as to enhance the information being provided to the customer. The project will involve analysis of data to enable various predictions, usage trends, efficiency improvements, reduce machine stress, predictive maintenance and a plethora of other possible outputs.
As an intern you will be embedded with the product and engineering teams in the RCT offices and will be able to contribute to the expansion of the fleet management product capabilities.
We are looking for a PhD student with the following:
- A degree in Engineering, Mathematics and Statistics or Data Science
- Demonstrated ability to assess and interpret large amounts of data and derive an outcome
- Demonstrated ability in mathematics and algorithm development
- Ability to visually present results of data analysis
- Programming skill in a programming language
- Proven report writing skills
- An understanding of industrial/mining vehicle operations
Overarching outcomes of this project are expected to be an initial understanding of the value of the raw data to the customer in terms of what productivity improvements are able to be made from the information gained on machine and operator performance. Specifically, we expect that the initial work will provide some basic understanding of which parameters are required, how much processing is required and some starting algorithms that will fuse essential data to provide productivity information. In addition, some concept on the best way to present the information (visual management of the results) would be fundamental in making the information useful to the end user.
The intern will receive $3,000 per month of the internship, usually in the form of stipend payments.
It is expected that the intern will primarily undertake this research project during regular business hours, spending at least 80% of their time on-site with the industry partner. The intern will be expected to maintain contact with their academic mentor throughout the internship either through face-to-face or phone meetings as appropriate.
The intern and their academic mentor will have the opportunity to negotiate the project’s scope, milestones and timeline during the project planning stage.
27 March 2019
APR – 0857