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Optimizing sample length for fault diagnosis of clutch systems using deep learning and vibration analysis

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Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering

Published online on

Abstract

Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, Volume 240, Issue 3, Page 2687-2697, June 2026.
Clutches are prone to failure owing to extended heat exposure and high levels of abrasion during power transfer. Internal damage, downtime, and permanent transmission system lock-up all can result from these faults. To detect and diagnose these faults, ...