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: View Website. Recently the UK Government made available the Doctoral Student Loans of up to £25,000 for UK and EU 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%.)
Below are some publications from my group. These will give you good indications of the work we have done already and the developments of our ideas, techniques, and implementations.
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.
FTE Category A staff submitted: 63.45
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