About the Project
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.
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.
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