Acoustic-based fault diagnosis of electric motors using Mel spectrograms and convolutional neural networks

Abstract This study presents a comprehensive deep learning framework for diagnosing acoustic faults in electric motors. The framework uses Mel spectrograms and a lightweight convolutional neural network (CNN). The method classifies three motor states, engine_good, engine_broken, and engine_heavyload...

Fuld beskrivelse

Bibliografiske detaljer

Principais autores:	Hasan Uzel, Yıldırım Özüpak, Feyyaz Alpsalaz, Emrah Aslan, Ievgen Zaitsev
Format:	Artigo
Sprog:	Inglês
Udgivet:	Nature Portfolio 2025-12-01
Serier:	Scientific Reports
Fag:	Acoustic fault diagnosis Mel spectrograms CNN Motor condition monitoring
Online adgang:	https://doi.org/10.1038/s41598-025-33269-z
Tags:	Tilføj Tag Ingen Tags, Vær først til at tagge denne postø!