Intelligent Algorithms For Signature Diagnostics Of Three-Phase Motors

TL;DR

This paper presents a hybrid machine learning approach combining supervised algorithms with unsupervised anomaly generation, leveraging physics models to improve the accuracy and reliability of three-phase motor diagnostics.

Contribution

It introduces a novel hybrid diagnostic method that integrates advanced ML with physics-based anomaly generation for better engine fault detection.

Findings

Significantly outperforms existing ML and non-ML methods

Achieves higher diagnostic accuracy and reliability

Demonstrates practical industrial applicability

Abstract

The application of machine learning (ML) algorithms in the intelligent diagnosis of three-phase engines has the potential to significantly enhance diagnostic performance and accuracy. Traditional methods largely rely on signature analysis, which, despite being a standard practice, can benefit from the integration of advanced ML techniques. In our study, we innovate by combining state of the art algorithms with a novel unsupervised anomaly generation methodology that takes into account physics model of the engine. This hybrid approach leverages the strengths of both supervised ML and unsupervised signature analysis, achieving superior diagnostic accuracy and reliability along with a wide industrial application. Our experimental results demonstrate that this method significantly outperforms existing ML and non-ML state-of-the-art approaches while retaining the practical advantages of an unsupervised methodology. The findings highlight the potential of our approach to significantly contribute to the field of engine diagnostics, offering a robust and efficient solution for real-world applications.