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The experimental results show that the method is able to estimate the alternator speed accurately even during fast mechanical transients.
The result shows that the faster the alternator spin, the more power it can produce.
These highlighted the need for rigorous battery charge management as the battery voltage was found not to be an appropriate measure of the variation in the alternator loading.
The paper demonstrates how artificial neural networks can be used for the online detection of interturn faults on the rotor of an alternator.
Therefore, a mechanical model of the alternator has to be developed to determine its main modes.
Hence, the proposed procedure is capable of detecting the alternator faults, and thus can be used for practical applications.
Open accessProceedings ArticleDOI
06 Jun 2004
7 Citations
The predicted alternator temperatures are found to be consistent with experimental measurement.
The predicted alternator temperatures are found to be consistent with the experimental measurement.
The theoretical results have been found to be in good agreement with available test data on the alternator.

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Factoring heat in induction motors can indeed help identify faults. Excessive heat generation in the motor's magnetic circuit, often due to faults like unbalanced voltage, broken rotor bars, or bearing issues, can be a key indicator of underlying problems. Thermal imaging techniques, such as Grey Level Co-occurrence Matrices (GLCM) and Raman Distributed Temperature Sensing (RTDS), are utilized to detect temperature variations associated with faults. The rise in temperature signifies abnormal energy losses, which can lead to insulation damage and short circuits. By analyzing the thermal signatures and patterns, along with employing machine learning models and algorithms like Extreme Gradient Boosting (XGBoost) and Random Forest (RF), faults can be accurately identified and classified. Therefore, monitoring heat patterns is a valuable method for fault detection in induction motors.