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.
- 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).
- 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.
- 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.
- 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.
- 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.
- 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:
- 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.
- 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.
- Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.
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%.
Meet the Supervisor(s)
- In April 2013, Professor Nandi moved to Brunel University London to become the Head of Electronic and Computer Engineering from the University of Liverpool where he held the David Jardine Chair of Signal Processing in the Department of Electrical Engineering and Electronics. At Liverpool he was the Head of the Signal Processing and Communications Research Group which he established in 1999. Professor Nandi received his PhD from the University of Cambridge. Subsequently, he held positions in Rutherford Appleton Laboratory, CERN, Queen Mary University of London, the University of Oxford, Imperial College London, University of Strathclyde, and the University of Liverpool.
Professor Nandi has published over 250 papers in refereed international journals (total: 600 technical papers) with an h-index of 80 (all citation figures are from Google Scholar) and the ERDOS number of 2. He co-discovered the three particles known as W+, W-, and Z0 - three of the four quanta of the electroweak force. This discovery verified the unification of the electromagnetic force and the nuclear weak force. In its recognition the 1984 Nobel Prize for Physics was awarded to his two leaders for their decisive role in this project. He has made pioneering theoretical and applied contributions to statistical signal processing, wireless communications, machine learning, and biomedical signal processing, image processing, genomic signal processing, brain signal processing, and Big Data.
Professor Nandi is a Fellow of the Royal Academy of Engineering as well as seven other institutions. He was an IEEE Distinguished Lecturer (EMBS, 2018-2019)