I. Andrijauskas “Investigation of electric machine diagnostic system” doctoral dissertation defence

Thesis Defense

Author, Institution: Ignas Andrijauskas, Kaunas University of Technology

Science area, field of science: Technological Sciences, Electrical and Electronics Engineering, T001

Scientific Supervisors:
Prof. Dr. Rimas Adaškevičius (Kaunas University of Technology, Technological Sciences, Electrical and Electronics Engineering, T001) 2017– 2019.
Assoc. Prof. Dr. Vytautas Šiožinys (Kaunas University of Technology, Technological Sciences, Electrical and Electronics Engineering, T001) 2014– 2017.

Dissertation Defence Board of Electrical and Electronics Engineering Science Field:
Dr. Liudas Mažeika (Kaunas University of Technology, Electrical and Electronics Engineering T001) – chairman;
Dr. Renaldas Raišutis (Kaunas University of Technology, Electrical and Electronics Engineering T001);
Dr. Antans-Saulus Sauhats (Riga Technical University, Electrical and Electronics Engineering T001);
Dr. Dainius Udris(Vilnius Gediminas Technical University, Electrical and Electronics Engineering T001);
Dr. Algimantas Valinevičius (Kaunas University of Technology, Electrical and Electronics Engineering T001).

For attendance of remote dissertation defence please join Zoom  meeting room.

The doctoral dissertation is available on the internet and at the library of Kaunas University of Technology (K. Donelaičio g. 20, Kaunas).

Annotation:

The aim of the presented study is the development of a new methodology for asynchronous motor fault diagnosis while using the well known and/or newly developed methods. The developed tools must enable ability to detect mechanical faults by use of the stator current signal. After the completion of simulating progressive unbalance, detailed dependency between information entropy and the level of unbalance has been revealed. No information has been detected in the reviewed scientific literature about the presented type of study. For the first time, the Neighborhood Component Feature Selection (NCFS) method has been applied for the selection of features for single-point fault diagnosis. The method for generalized roughness bearing fault diagnosis by the use of the stator current signal has been specified. No information has been noted on successive fault diagnosis with the use of the stator current signal.

Download Premium WordPress Themes Free
Download Premium WordPress Themes Free
Download WordPress Themes
Free Download WordPress Themes
udemy course download free
We are using cookies to provide statistics that help us give you the best experience of our site. You can find out more or switch them off if you prefer. However, by continuing to use the site without changing settings, you are agreeing to our use of cookies.
I agree