A software engineer from Srinakharinwirot University (SWU) has released a technical video demonstrating the application of a neuro-fuzzy inference system for detecting faults in electric motors. The system integrates fuzzy logic with neural networks to analyze motor behavior and identify anomalies, offering a hybrid AI solution for predictive maintenance.
"The neuro-fuzzy approach combines the interpretability of fuzzy rules with the learning capability of neural networks, making it effective for complex diagnostic tasks," the engineer notes in the video description.
The work is part of ongoing research at SWU's Computational Intelligence Research Lab (CIRL), which focuses on artificial intelligence, machine learning, and fuzzy systems. While the video is geared toward education and knowledge sharing, it highlights the growing role of AI in industrial applications.
Although the video itself is not publicly accessible, the description indicates that the method can generalize to other domains, such as battery and fuel usage optimization in hybrid vehicles, suggesting a broader potential for neuro-fuzzy systems in engineering diagnostics.