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Machine condition diagnosis and prognosis

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

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.

Funding Notes

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%.)

References

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.

How good is research at Brunel University London in General Engineering?

FTE Category A staff submitted: 63.45

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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