Jul 2026· 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT)· pp. 780-785· 0 citations· 16 references
Abstract
Condition monitoring of electrical machines has garnered continuous study attention for more than thirty years, especially concerning spinning electrical machinery. Previously, operators meticulously monitored machine performance; however, this practice has diminished with the introduction of rapid-response digital protection mechanisms. This study, grounded in the author’s expertise and current literature, concentrates on online monitoring techniques, with minimal attention to variable speed drives and a preference for conventional machines over contemporary topologies. Data preparation utilises the DWT to efficiently identify and extract unique signal patterns. SVM-ANN are employed for classification, exemplified by a binary decision problem of fault versus no fault classification in early-stage virtual screening. The findings validate that DWT is an effective instrument for feature extraction in condition monitoring. Moreover, SVM exhibits superior performance with diminished standard error in comparison to ANN. SVM-ANN model consistently surpasses alternative methods with an accuracy of 96.06% regardless of training data volume, neural network techniques, or descriptor types. The amalgamation of wavelet-based methodologies with machine learning models demonstrates significant efficacy in the Condition Monitoring of Electrical Machines, facilitating dependable fault detection and enhanced predictive capabilities, while underscoring the necessity for further research on contemporary machine designs and variable speed applications.
The results indicate that phase-angle information provides supplementary and class dependent discriminative value, but does not consistently improve all fault classes, whereas conventional voltage and current measurements alone represent a simpler and more stable alternative, whereas phase-angle measurements may be incorporated when synchronized phasor information is already available.
Zeynep Bala Duranay, İsmail Anıl Avcı, Mohammed Bushra Mohammed et al.· Symmetry· 0 citations
The proposed framework employs ANN as a nonlinear feature embedding and a Radial Basis Function SVM subsequently classifies using an Error-Correcting Output Codes (ECOC) strategy, validating the effectiveness of the proposed hybridisation strategy for intelligent transmission system protection.
Kudu Abubakar Mohammed, M. Balogun, Adesina M. Lambe et al.· Communication in Physical Sc...· 0 citations
The outcomes prove that the integrated technical framework (time-domain features + FR + SVM) provides zero false alarms and a balanced diagnostic system that combines computational speed with high precision.
H. Zaimen, T. Thelaidjia, Makhlouf Chouki et al.· International Journal of Ele...· 0 citations
Introduction. Bearing faults in induction motors are one of the primary causes of performance degradation and unexpected failures in industrial systems. Early fault detection remains challenging because conventional protection systems generally respond only after severe damage occurs. In addition, motor current signals exhibit nonlinear and complex characteristics, requiring advanced analysis techniques for accurate fault identification. Problem. Existing fault diagnosis methods often suffer from limited classification accuracy, dependency on specific operating conditions, and insufficient integration between spectral feature extraction and adaptive classification techniques. Goal. To develop a non-invasive bearing fault classification method based on current spectrum analysis and artificial neural network (ANN) for induction motor condition monitoring. Methodology. The proposed method utilizes fast Fourier transform (FFT) to transform motor current signals from the time domain into the frequency domain for spectral feature extraction. The extracted features are then processed using principal component analysis (PCA) for dimensionality reduction before being used as inputs to the ANN classifier. Experimental testing is conducted under 3 bearing conditions, namely normal, 7-ball fault, and 6-ball fault conditions, using 50 datasets for each condition. Results. The results demonstrate that the proposed method successfully identifies bearing conditions with high classification accuracy and strong separation characteristics in the PCA space. FFT analysis also reveals consistent spectral changes corresponding to fault severity, particularly in sideband components and energy distribution patterns. Scientific novelty. This work integrates FFT-based current spectrum analysis, PCA-based feature reduction, and ANN classification into a unified non-invasive diagnosis framework for bearing fault detection. Practical value. The proposed approach provides a simple, adaptive, and reliable solution for early bearing fault detection without requiring additional mechanical sensors, making it suitable for industrial condition monitoring applications. References 32, tables 4, figures 7.
O. A. Qudsi, E. Purwanto, S. M. I. Taufik et al.· Electrical Engineering &...· 0 citations
Experimental results indicate that XGBoost achieves the highest accuracy in identifying unbalance, outperforming the neural network and the Bayesian model and for misalignment detection, however, the three methods exhibit comparable performance, underscoring the limitations of ML models that rely solely on vibration indicators for this fault type.
A. Marzougui, A. Hachem, T. Mazoyer· Insight - Non-Destructive Te...· 0 citations
The diagnosis framework constructed in this study effectively reduces the dependence on manual experience and provides technical support for improving the safety and operation level of building electrical systems.
Hua Liu· Engineering Research Express· 0 citations
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