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Machine condition diagnosis and prognosis

Smooth running of machines are vital in all sectors of manufacturing. In recent years much emphasis are put on the development of artificial intelligence techniques to solve issues related to machine condition diagnosis and prognosis. This is now very much in the heart of smart manufacturing. To avoid the burden of much storage requirements and processing time, compressive sampling with correlated principal and discriminant components for bearing faults diagnosis based on compressed measurements has been proposed. The project will review existing techniques and develop new and improved ones. Aims are to achieve high classification accuracies, reduced computation time, and storage requirement, with fewer measurements.

This project will involve programming, signal processing, machine learning, mathematical analysis, and good writing ability for presentation of technical work. An ideal candidate will have a very good Master degree or a First Class Bachelor degree. Below are some publications from my group. These will give you some indications of the work we have done already.

  1. H Ahmed and A K Nandi, "Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines", Published by John Wiley & Sons, Chichester, West Sussex, UK, 2020 (ISBN 978-1-119-54462-3).
  2. Y Lei, B Yang, X Jiang, F Jia, N Li, and A K Nandi, "Application of machine learning to machine fault diagnosis: A review and roadmap", Mechanical Systems and Signal Processing, DOI: 10.1016/j.ymssp.2019.106587, vol. 138, pp. ?-?, 2020.
  3. H Ahmed and A K Nandi, "Three-stage Hybrid Fault Diagnosis for Rolling Bearings with Compressively-sampled data and Subspace Learning Techniques", IEEE Transactions on Industrial Electronics, DOI: 10.1109/TIE.2018.2868259, vol. 66, no. 7, pp. 5516-5524, 2019.
  4. H Ahmed and A K Nandi, "Compressive sampling and feature ranking framework for bearing fault classification with vibration signals", IEEE Access, DOI: 10.1109/ACCESS.2018.2865116, vol. 6, no. 1, pp. 44731-44746, 2018.
  5. H O A Ahmed, M L D Wong, and A K Nandi, "Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features", Mechanical Systems and Signal Processing, DOI: 10.1016/j.ymssp.2017.06.027, vol. 99, pp. 459-477, 2018.
  6. M Seera, M L D Wong, and A K Nandi, "Classification of ball bearing faults using a hybrid intelligent model", Applied Soft Computing, DOI: 10.1016/j.asoc.2017.04.034, vol. 57, pp. 427-435, 2017.

This project will involve programming, signal processing, machine learning, mathematical analysis and require good writing ability for presentation of technical work.

An ideal candidate will have a very good Master degree or a First Class Bachelor degree.

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

  1. Contact the supervisor by email or phone to discuss your interest and find out if you woold be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
  2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
  3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.