The KAN-HyperMP model is composed of three key components: a neighbor feature aggregation block, a feature fusion block, and a KANLinear block. Firstly, the ...
Oct 5, 2024 · Rolling bearings often produce non-stationary signals that are easily obscured by noise, particularly in high-noise environments, ...
The KAN-HyperMP model is composed of three key components: a neighbor feature aggregation block, a feature fusion block, and a KANLinear block. Firstly, the ...
The proposed model is trained and evaluated on the Southeast University (SEU) and Jiangnan University (JNU) Datasets, achieving accuracy rates of 99.70% and ...
sensors. Article KAN-HyperMP: An Enhanced Fault Diagnosis Model for. Rolling Bearings in Noisy Environments Jun Wang 1 , Zhilin Dong 2, * and Shuang Zhang 3.
KAN-HyperMP: An Enhanced Fault Diagnosis Model for Rolling Bearings in Noisy Environments. Sensors 2024, 24, 6448. https://doi.org/10.3390/s24196448. AMA ...
Triplet attention-enhanced residual tree-inspired decision ... KAN-HyperMP: An Enhanced Fault Diagnosis Model for Rolling Bearings in Noisy Environments.
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Dec 9, 2024 · KAN-HyperMP: An Enhanced Fault Diagnosis Model for Rolling Bearings in Noisy Environments. Article. Full-text available. Oct 2024; SENSORS-BASEL.
KAN-HyperMP: An Enhanced Fault Diagnosis Model for Rolling Bearings in Noisy Environments. Article. Full-text available. Oct 2024; SENSORS-BASEL. Jun Wang ...
KAN-HyperMP: An Enhanced Fault Diagnosis Model for Rolling Bearings in Noisy Environments ... rolling bearing fault diagnosis under limited sample conditions.