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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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Condition Monitoring of Induction Machines: Quantitative Analysis and Comparison.

Michele Sintoni¹, Elena Macrelli¹, Alberto Bellini¹

¹Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" (DEI), Alma Mater Studiorum, University of Bologna, 47522 Cesena, Italy.

Sensors (Basel, Switzerland)

|January 21, 2023

Summary

This study introduces an improved diagnostic method for detecting rotor bar faults in induction motors, especially during variable speed drive operations. The new technique enhances fault detection accuracy under transient and time-varying conditions.

Keywords:

electric machines fault diagnosis wavelet transforms

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Area of Science:

Electrical Engineering
Mechanical Engineering
Condition Monitoring

Background:

Induction motors are critical in industrial applications, but traditional monitoring methods struggle with variable speed drives and load oscillations.
Rotor bar faults can lead to motor failure, necessitating reliable detection techniques, especially in dynamic operating conditions.

Purpose of the Study:

To present a novel diagnostic procedure for early detection of rotor bar faults in induction motors.
To enhance the accuracy of fault detection, particularly in transient and time-varying operational states common in variable speed drive applications.

Main Methods:

The proposed method combines the Hilbert transform and discrete wavelet transform to analyze motor signals.
It specifically computes energy within a bandwidth that captures the maximum fault signature.
The approach is validated against time-domain, frequency-domain, and time-frequency analysis methods using identical datasets.

Main Results:

The Hilbert and discrete wavelet transform-based method demonstrates superior performance in detecting rotor bar faults compared to existing techniques.
The enhanced detection capability is particularly evident under transient and time-varying operating conditions.
Quantitative analysis confirms the improved performance and aids in optimizing the balance between accuracy and computational cost.

Conclusions:

The presented diagnostic procedure offers a significant improvement for identifying rotor bar faults in induction motors, especially in challenging industrial environments.
This method provides a robust solution for condition monitoring in variable speed drive systems.
The combined Hilbert and discrete wavelet transform approach is recommended for its enhanced sensitivity and effectiveness in dynamic scenarios.