Performance and time comparison of different models on the Gearbox Fault Diagnosis dataset (mean std).

Performance and time comparison of different models on the Gearbox Fault Diagnosis dataset (mean std).

<p>Performance and time comparison of different models on the Gearbox Fault Diagnosis dataset (mean std).</p>

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Bibliographic Details
Main Author:	Van-Trang Nguyen (21673190) (author)
Other Authors:	Quoc Bao Diep (18452207) (author)
Published:	2025
Subjects:	Biotechnology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified wise feature concatenation variable operating conditions time fourier transform reduce maintenance costs proposed method well proposed method addresses power generation facilities improve equipment availability extract discriminative features deep learning architectures based maintenance strategies significantly higher accuracy enhance diagnostic accuracy computational efficiency renders early fault detection accurate fault detection mixnet utilizes multi high accuracy operational efficiency xlink "> stft ). spectrogram representations specific expertise severe cases robust performance paramount importance novel approach minimizes reliance malfunctions leading invasive monitoring industrial environments industrial challenges including non incipient faults heavy machinery generated via facilitates condition experimental results engineering applications energy production critical components costly downtime catastrophic incidents automotive applications automated diagnosis article proposes 4 minutes 29 seconds
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Summary:	<p>Performance and time comparison of different models on the Gearbox Fault Diagnosis dataset (mean std).</p>

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