Indoor positioning of Assets monitoring and life cycle of equipment using Machine-learning/AI

   Faculty of Science and Engineering

  ,  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Right now, all efforts are being made to bring about the Industry 4.0 revolution. Many companies are now focusing on predictive maintenance, for which they need to collect data from every piece of equipment inside a factory. This information can be quickly gathered from some permanently installed machines. However, everything that can move throughout the factory, including the elevators, excavators, escalators, and power tools, it is a challenge to collect positioning data. Using example of an elevator, it will start acting erratically if there is a problem with any of its components. The elevator may start moving in the incorrect direction, slow down or speed up significantly, or simply fail to stop at the correct floor. Some of the research in this area employs multiple sensors and some wireless sensor parameters to determine position of moving equipment such as elevator based on signal strength. In some cases, barometer, gyroscope, and accelerometer combinations are used to determine position in the vertical direction. However, the indoor positioning as a research field is emerging and there is a great deal of potential to do impactful research.

The precise identification of horizontal and vertical positioning is very useful in various situations, such as developing robots to find individuals under a demolished building due to a disaster, or use case of monitoring factory workers, or when a business needs to tally mobile asset usage, etc.

This PhD research will focus on building novel techniques to support indoor positioning of Assets monitoring and life cycle of equipment using Machine-learning/AI.

The candidate will join a diverse group of researchers engaged in Artificial Intelligence, Deep Learning, Semantic Web, Smart Cities, Industry 4.0 and Internet of Things (IoT) research streams. The group members already work on world-leading research in this area in the context of funded projects ( Candidate will have option to apply their work in a number of applied domains including smart factories, smart cities, and emergy services, to name a few.

Eligibility and entry requirements

Applicants should have a minimum 2:1 degree in Computer Science or relevant subject. A taught MSc or Masters by Research in a relevant subject or relevant laboratory experience would be an advantage.

How to apply IMPORTANT - Please include the project title and proposed supervisor in your application.

Funding Notes

Self-funded PhD students only.

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