Lightweight Adaptive Feature Compression and Dynamic Network Fusion for Rotating Machinery Fault Diagnosis Under Extreme Conditions

Reliable fault diagnosis of rotating machines under extreme conditions—strong speed, load variation, intense noise, and severe class imbalance—remains a critical industrial challenge. We develop an ultra-light yet robust framework to accurately detect weak bearing, and gear faults when less than 5%...

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Auteurs principaux:	Kaiyi Zhang, Xuling Liu, Guohua Yang, Kun Zhai, Gaofei An, Yusong Zhang, Chaofeng Peng
Format:	Artigo
Langue:	Inglês
Publié:	MDPI AG 2025-09-01
Collection:	Actuators
Sujets:	<i>K-means</i> feature module clustering workload-adaptive multi-scale convolution self-attention dynamic GRU auto-encoder extreme class imbalance rotating machinery fault diagnosis
Accès en ligne:	https://www.mdpi.com/2076-0825/14/9/458
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