Anomaly Detection in Brushless Fan Systems using integration of wavelet approach and convolutional neural network
DOI:
https://doi.org/10.31663/utjes.14.1.673Keywords:
Anomaly detection, deep learning, Convolutional Neural Networks (CNN), wavelet analysisAbstract
In this research, trustworthiness is improved, and the probability of malfunctioning in DC fan systems is decreased was addressed. This research suggests a new method that employs artificial intelligence to detect faults by combining Convolutional Neural Networks (CNN) with wavelet analysis for better anomaly detection. Ten fans were initially used in the experiment; three were faulty, and seven operated correctly. Data for all fans was collected using an Arduino. Wavelet features were employed to identify faults, followed by a CNN classifier (. Specifically, a binary CNN classifier) to determine the presence or absence of faults. The results were very promising, demonstrating significant potential for improving the reliability of DC fan systems.
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