LD-RPMNet combines depthwise separable multi-scale convolution and broadcast self-attention to reach 98.86% accuracy on railway point machine sound data with 0.48M parameters, though the reported metrics are internally inconsistent.
A nonlinear dynamics method using multi-sensor signal fusion for fault diagnosis of rotating machinery,
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LD-RPMNet: Near-Sensor Diagnosis for Railway Point Machines
LD-RPMNet combines depthwise separable multi-scale convolution and broadcast self-attention to reach 98.86% accuracy on railway point machine sound data with 0.48M parameters, though the reported metrics are internally inconsistent.