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Corresponding Author

IBRAHIM MUHAMMAD

Subject Area

Electrical Engineering

Article Type

Review

Abstract

Feature selection plays a critical role in designing efficient and interpretable condition monitoring frameworks for electrical drives. In this paper, a correlation analysis of statistical and spectral features is performed for Permanent Magnet Synchronous Motor (PMSM) fault detection in naval windlass systems. Using both simulated data from a MATLAB/Simulink model and real shipboard current signals acquired from five Nigerian Navy vessels over one-month monitoring periods, higher-order statistical moments (Mean, Variance, Standard Deviation, Skewness, Kurtosis) and the Fault Severity Index (FSI) were computed alongside Total Harmonic Distortion (THD). Pearson correlation coefficients were employed to quantify feature relationships under healthy and faulty operating modes, while scatter-plot clustering was used to visualize feature separability across six fault classes. The dataset comprised 2,880 observation windows per vessel (2 kHz sampling, 60-minute windows with 30-minute overlap), yielding a total analytical corpus of 14,400 windows from ship data and 12,000 high-resolution windows from simulation. Results demonstrated strong correlations between variance, standard deviation, and FSI (r ≥ 0.80–0.99), confirming their redundancy. Kurtosis and skewness exhibited weaker correlations with other first- and second-order features (r < 0.60) but showed a moderate inter-correlation of r = 0.88 with each other, indicating shared higher-order sensitivity. THD demonstrated weak correlations with all time-domain statistical moments (r < 0.65), confirming its role as an independent spectral indicator. All reported coefficients were statistically significant (p < 0.001, df = n − 2). The study highlights the potential for dimensionality reduction in PMSM diagnostic frameworks without compromising detection accuracy, with practical guidance for real-time embedded naval monitoring systems.

Keywords

PMSM, Fault Detection, Statistical Feature Extraction, Feature Correlation Analysis, Kurtosis, Skewness.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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