The goal of this project is to investigate, design and implement machine learning algorithms for the purpose of anomaly and fault detection in vibrating mechanical systems in real-time. This investigation includes working in vibrations lab and hands-on experience with experimental measured data, as well as implementing the AI algorithms using Python, C++, and MATLAB. The core of the project will be collecting and analysing measurement data from vibrating mechanical systems (rotating equipment mostly) and design and development of required machine learning algorithms to identify known faults by classification methods, as well as unknown faults by clustering methods. Ultimately the best suited algorithms are to be implemented in a prototype “automated” system that can notify the user of emerging failures and provide a framework for predictive maintenance of the mechanical system. The data collected throughout the project will then be used for generating data-based scheduled maintenance procedures for the operators. The successful candidate will need to demonstrate:
- Excellent written and verbal communication skills.
- Strong computational, programming, algorithms, and data analysis skills.
- Outstanding research skills
- Capacity to work independently and as a part of a team."