Defence of doctoral thesis in the field of Electrical Power and Energy Engineering, M.Sc. Bilal Asad
M.Sc. Bilal Asad will defend the thesis "Mathematical Modelling of Three Phase Squirrel Cage Induction Motor and Related Signal Processing for Fault Diagnostics" on 27 August 2021 at 14:00. The doctoral thesis has been completed under cotutelle agreement between Aalto University School of Electrical Engineering and Tallinn University of Technology, Estonia. The public defence will take place in TalTech.
Prof. Jose Antonino-Daviu, Polytechnic University of Valencia, Spain
Prof. Lucia Frosini, University of Pavia, Italy
Prof. Anouar Belahcen, Aalto University
Prof. Toomas Vaimann, Tallinn University of Technology, Estonia
Custos in Aalto University: Prof. Anouar Belahcen, Aalto University School of Electrical Engineering, Department of Electrical Engineering and Automation
The public defense will be organized via remote technology. Follow defence:https://zoom.us/j/95915675538?pwd=MVE3SXBkUVhEV3Rxa0ZFT3JjN0tvZz09
Zoom Quick Guide: https://www.aalto.fi/en/services/zoom-quick-guide
Thesis available for public display at: https://aaltodoc.aalto.fi/doc_public/eonly/riiputus/
Doctoral theses in the School of Electrical Engineering: https://aaltodoc.aalto.fi/handle/123456789/53
Electrical machines, particularly induction motors, are indispensable in almost all sectors of our modern society. They power applications such as conveyor belt movers, compressors, electric vehicles, fans, and pumps, and they consume more than 50% of the total generated electrical energy worldwide. The predictive maintenance of electrical machines is very important to avoid any system-level catastrophic situation related to their malfunction. As the world is moving towards industry 4.0, predictive maintenance is becoming more suitable than preventive or reactive maintenance. Unlike preventive or reactive maintenance, the predictive maintenance monitors the behavior of an electrical machine and anticipates failures before they occur; it allows servicing the machine as needed. By doing so, the system’s downtime is decrease as well as the maintenance cost.
Diagnostics-compatible mathematical models are becoming increasingly popular for predictive maintenance of electrical machines. These models can be used for machine parameters estimation, simulation of various faults for the training of artificial intelligence based diagnostic algorithms, development of digital twins, and hardware in the loop environment. However, the computational complexity and long simulation time hinder the utilization of accurate numerical models such as the finite element method, whereas simple analytical models are not accurate enough for this purpose. This thesis proposes accurate analytical models of induction machines with reduced simulation time and complexity. The proposed models can simulate different faults and can be easily parallelized. Furthermore, various signal processing techniques for segregating different faults from the machine current are also presented and discussed in this thesis. T
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