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Intelligent Algorithms For Signature Diagnostics Of Three-Phase Motors

Stepan Svirin, Artem Ryzhikov, Saraa Ali, Denis Derkach

TL;DR

This study innovate by combining state of the art algorithms with a novel unsupervised anomaly generation methodology that takes into account physics model of the engine, achieving superior diagnostic accuracy and reliability along with a wide industrial application.

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.

Intelligent Algorithms For Signature Diagnostics Of Three-Phase Motors

TL;DR

This study innovate by combining state of the art algorithms with a novel unsupervised anomaly generation methodology that takes into account physics model of the engine, achieving superior diagnostic accuracy and reliability along with a wide industrial application.

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.

Paper Structure

This paper contains 18 sections, 4 equations, 1 figure, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. 3-phase engines
  4. Engine Defect Types
  5. Intercell Shortages
  6. Rotor Cell Defect
  7. Bearing Defect
  8. Eccentricity of the Air Gap
  9. Unknown Mechanical Defects
  10. Existing diagnosis approaches. Signature Analysis
  11. ML Approaches
  12. Existing ML approaches limitations
  13. Methodology
  14. Proposed method
  15. Conclusion
  16. ...and 3 more sections

Figures (1)

  • Figure 1: Signature method illustration signature. On the left side, the engine current over time is shown. On the right side, the corresponding Fourier spectrum of the current is displayed. The first row shows a healthy engine's current and spectrum, which has only a single peak at the operating frequency. The next two rows correspond to engine defects and exhibit anomalous peaks in their Fourier spectra.