Intelligent Algorithms For Signature Diagnostics Of Three-Phase Motors

Student: Emiliya Iskhakova

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2024

Equipment failures in industry can significantly reduce the efficiency, stop production, and companies incur high costs. Meanwhile, predicting and preventing breakdowns will help solve this problem. To date, there are no intelligent diagnostic methods for three-phase induction motor equipment either on the market or in science. All publications on the diagnosis of threephase motors are reduced either to a signature method that is unrelated to machine learning and based on physical properties, or to logistic regression. In this work, new methods for detecting breakdowns of three-phase motors are proposed, data on which were obtained from production facilities. These methods are based on various approaches to anomaly detection, including methods using Fourier transform and deep learning.

Student Theses at HSE must be completed in accordance with the University Rules and regulations specified by each educational programme.

Summaries of all theses must be published and made freely available on the HSE website.

The full text of a thesis can be published in open access on the HSE website only if the authoring student (copyright holder) agrees, or, if the thesis was written by a team of students, if all the co-authors (copyright holders) agree. After a thesis is published on the HSE website, it obtains the status of an online publication.

Student theses are objects of copyright and their use is subject to limitations in accordance with the Russian Federation’s law on intellectual property.

In the event that a thesis is quoted or otherwise used, reference to the author’s name and the source of quotation is required.

Search all student theses