Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- The University of Auckland
- Cranfield University
- ;
- University of Tübingen
- Technical University of Denmark
- ; Newcastle University
- ; University of Warwick
- Curtin University
- Forschungszentrum Jülich
- Nature Careers
- University of Adelaide
- University of British Columbia
- University of Groningen
- University of Luxembourg
- University of Twente
- ; Cranfield University
- ; Technical University of Denmark
- ; The University of Edinburgh
- ; The University of Manchester
- ; University of Oxford
- ; University of Reading
- ; University of Sheffield
- Crohn’s & Colitis Australia IBD PhD Scholarship
- Ghent University
- NTNU - Norwegian University of Science and Technology
- RMIT University
- Technical University of Munich
- University of Copenhagen
- University of Southern Denmark
- University of Texas at Austin
- Vrije Universiteit Brussel
- Østfold University College
- 22 more »
- « less
-
Field
-
This PhD at Cranfield University explores the development of resilient, AI-enabled electronic systems capable of detecting faults and autonomously recovering from failures in real time. The project
-
radiation-induced faults. This project aims to develop an optimised and fault-tolerant version of Falcon for such environments. We will simplify complex operations, speed up critical steps using custom
-
, increasing costs and resource waste. This Ph.D. project aims to address these challenges by advancing fault diagnosis and prognosis (FDP) for complex mechatronic systems. Building on Supervisor Team's
-
emission analysis, wear and oil analysis,…,etc. have been used, through condition monitoring of the rotating machinery, to diagnose and prognosis different faults such as, bearing, crack shaft, gearbox
-
downtime and operational costs. Traditional condition monitoring approaches often face challenges in accurately detecting early-stage faults, especially in the presence of highly impulsive signals
-
control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
-
, while simulations are subject to error due to uncertainty in nuclear data and unresolved physical processes e.g. thermal expansion and fine-scale inhomogeneities. Generating independent simulation
-
Fuel Rig with Five Degradation Faults: Simulates various degradation scenarios in unmanned aerial vehicle (UAV) fuel systems, enabling research into fault detection, isolation, and prognostics. Machine
-
Degradation Faults: Simulates various degradation scenarios in unmanned aerial vehicle (UAV) fuel systems, enabling research into fault detection, isolation, and prognostics. Machine Fault Simulator
-
suite of specialised facilities: UAV Fuel Rig with Five Degradation Faults: Simulates various degradation scenarios in unmanned aerial vehicle (UAV) fuel systems, enabling research into fault detection