Intelligent Algorithms For Signature Diagnostics Of Three-Phase Motors

Traditional diagnostic methods for these engines predominantly rely on signature analysis, a technique that examines the engine's operational patterns to detect anomalies [1]. While signature analysis has become a de-facto standard due to its effectiveness, it has some substantial limitations, and the growing complexity of modern engines and the vast amounts of data they generate require more advanced and precise diagnostic frameworks [2]. At the same time, machine learning (ML) and artificial intelligence (AI) have emerged as essential tools integrated into various aspects of modern life, from recommendation algorithms [3] to healthcare [4] applications. The potential for advancement and innovation in these fields is immense. Despite this, the application of ML in industrial settings remains underexplored, primarily due to the scarcity of publicly available labeled datasets, especially with malfunctioning engines This lack of data poses significant challenges when transitioning ML solutions from experimental phases to full-scale production, especially given the complexities and variability of real-world conditions [5].