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An effective torque-based method for automatic turn fault detection and turn fault severity classification in permanent magnet synchronous motor

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Abstract

This article presents a novel approach based on the electromechanical torque signal for the inter-turn short-circuit fault (ISCF) detection and the ISCF severity estimation in permanent magnet synchronous motors (PMSMs). The electromechanical torque data have been obtained experimentally in the healthy condition and in three various states of the ISCF at various load rates and at various operating speeds. To extract the features to be used in the ISCF diagnosis, the fast Fourier transform (FFT) implemented to the torque signal. The torque’s second and fourth harmonics were found to be new turn fault features that could be used for ISCF diagnosis. These features were used to train and test the classification algorithms. Four classification algorithms were used to detect ISCF and determine the severity of ISCF: decision trees (DT), artificial neural networks (ANN), K-nearest neighbor (KNN) and support vector machines (SVM). Classification accuracies of 100%, 99.30%, 97.91% and 95.48% were achieved by the ANN, SVM, KNN and DT classifiers, respectively. High accuracy ISCF detection and high accuracy ISCF severity estimation were performed using the developed diagnostic method based on the torque signal.

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Abbreviations

C:

The cost parameter

K:

The number of nearest neighbors

TP:

Correct classification

FN:

Incorrect classification

PMSM:

Permanent magnet synchronous motors

ML:

Machine learning

WT:

Wavelet transform

ISCF:

Inter-turn short-circuit fault

ANN:

Artificial neural networks

FFT:

Fast Fourier transform

SVM:

Support vector machines

MCSA:

Machine current signature analysis

DT:

Decision trees

KNN:

K-nearest neighbor

STFT:

Short-time Fourier transform

FEA:

Finite element analysis

1D-LBP:

One-dimensional local binary patterns

RF:

Random forests

VSI:

Voltage source inverter

DAQ:

Data acquisition systems

QDA:

Quadrature discriminant analysis

MFNN:

Multilayer feedforward neural network

MSE:

The mean square error

LDA:

Linear discriminant analysis

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  1. Department of Electrical and Electronics Engineering, Dicle University, 21280, Diyarbakir, Turkey

    Timur Lale & Bilal Gümüş

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Lale, T., Gümüş, B. An effective torque-based method for automatic turn fault detection and turn fault severity classification in permanent magnet synchronous motor. Electr Eng 106, 2865–2876 (2024). https://doi.org/10.1007/s00202-023-02113-w

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