End-of-Line Quality Control Based on Mel-Frequency Spectrogram Analysis and Deep Learning

End-of-Line Quality Control Based on Mel-Frequency Spectrogram Analysis and Deep Learning

This study presents a novel approach to the end-of-line (EoL) quality inspection of brushless DC (BLDC) motors by implementing a deep learning model that combines MEL diagrams, convolutional neural networks (CNNs) and bidirectional gated recurrent units (BiGRUs). The suggested system utilizes raw vi...

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Bibliographic Details
Main Authors: Jernej Mlinarič, Boštjan Pregelj, Gregor Dolanc
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Machines
Subjects:
end-of-line quality inspection
fault detection
vibration control
condition monitoring
deep learning
convolutional neural networks
Online Access:https://www.mdpi.com/2075-1702/13/7/626
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