A SOFTWARE SOLUTION FOR INTELLIGENT FAULT DETECTION IN WHEEL BEARINGS
| dc.contributor.author | Glugovskiy, Glib | |
| dc.date.accessioned | 2025-10-10T10:34:07Z | |
| dc.date.available | 2025-10-10T10:34:07Z | |
| dc.date.issued | 2025-01-23 | |
| dc.description.abstract | Wheel bearings are critical components in machinery and vehicles, and their failure can lead to costly downtime and safety risks. This project focuses on the research, development, and implementation of an intelligent software solution for fault detection in wheel bearings. By combining advanced signal processing techniques and machine learning, the solution enables early and accurate fault diagnosis efficiently for all users. The software incorporates Fast Fourier Transform (FFT) for feature extraction and Self-Organizing Maps (SOMs) for clustering and fault classification, achieving high accuracy in detecting bearing defects. This approach marks a significant advancement in the fields of machine maintenance and fault diagnostics by offering a more precise and efficient method for identifying defects in wheel bearings. A key aspect of the project involves the development and training of a SOM model. This model is specifically designed to analyze patterns in the cyclostationary signal data, enabling the accurate identification of potential bearing faults. The SOM model, along with the other signal processing and machine learning components, is integrated into a robust software application. Built using Django and React.js for the web interface and enhanced with Rust libraries for computational efficiency, this solution presents a novel integration of traditional web technologies with modern performance optimization techniques. | |
| dc.identifier.citation | Glugovskiy, Glib. (2025). A SOFTWARE SOLUTION FOR INTELLIGENT FAULT DETECTION IN WHEEL BEARINGS. Kyiv: American University Kyiv. URI: https://er.auk.edu.ua/handle/234907866/165 | |
| dc.identifier.uri | https://er.auk.edu.ua/handle/234907866/165 | |
| dc.language.iso | en_US | |
| dc.publisher | Manuscript | |
| dc.subject | Self-organizing maps | |
| dc.subject | Fault identification | |
| dc.subject | Cyclostationary signals | |
| dc.subject | Bearing Diagnostics | |
| dc.title | A SOFTWARE SOLUTION FOR INTELLIGENT FAULT DETECTION IN WHEEL BEARINGS | |
| dc.title.alternative | ПРОГРАМНЕ РІШЕННЯ ДЛЯ ІНТЕЛЕКТУАЛЬНОГО ВИЯВЛЕННЯ НЕСПРАВНОСТЕЙ У ПІДШИПНИКАХ КОЛІС | |
| dc.type | Thesis |