2026-05-06T09:27:29.674Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:3:11435 2025-06-07T22:17:23Z 3

Audio Analysis Optimized by Modified Metaheuristic for Motor Predictive Maintenance, Chapter in AIS Algorithms for Intelligent Systems: CVR 2024: Computer Vision and Robotics, Springer 2024 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/3/11435 https://link.springer.com/chapter/10.1007/978-981-97-8868-2_11 L. Jovanovic A. Bozovic N. Bacanin V. Radojcic M. Zivkovic M. Dobrojevic In model systems, the inclusion of motors and actuators is essential, especially in equipment such as spindle lathes, drills, and mills, where their appropriate operation is crucial. Because of their resilience to wear and low-maintenance needs, brushless DC motors are becoming indispensable in the contemporary industrial and transportation sectors. This is further highlighted by the fact that these motors are integrated into electric cars. In order to guarantee the reliability and lifespan of complex mechanical systems, maintenance is essential. Timely correction of minor wear is essential to forestall significant failures, although regular maintenance programs are not always sufficient to prevent all problems. This study presents a case for data-driven maintenance, emphasizing constant monitoring to identify any breakdowns almost immediately. This technology offers a proactive approach to maintenance problems in intricate mechanical systems by promising increased efficiency as well as preventing major machinery damage. The research uses FFT auto-analysis to find brushless DC motor faults and applies a variety of modern optimizers to evaluate the feasibility of the method. Furthermore, an especially adjusted optimizer is shown to fulfill the special needs of this research. When it comes to fault detection, optimized models produce almost flawless outcomes. bookPart Springer, Singapore 127 140 10.1007/978-981-97-8868-2_11 AIS Algorithms for Intelligent Systems: CVR 2024: Proceedings of Computer Vision and Robotics