A real-world multi-modal Wi-Fi fault dataset and unified benchmark are introduced to evaluate diagnosis approaches across tasks, modalities, and LLM-based reasoning.
Graph neural network-based fault diagnosis: a review
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A multi-level temporal graph network with LSTM, graph convolutions, multi-level pooling, and local-global fusion outperforms baselines on the Tennessee Eastman process for industrial fault diagnosis.
citing papers explorer
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Toward Realistic Wi-Fi Fault Diagnosis: A Multi-Modal Benchmark
A real-world multi-modal Wi-Fi fault dataset and unified benchmark are introduced to evaluate diagnosis approaches across tasks, modalities, and LLM-based reasoning.
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Multi-Level Temporal Graph Networks with Local-Global Fusion for Industrial Fault Diagnosis
A multi-level temporal graph network with LSTM, graph convolutions, multi-level pooling, and local-global fusion outperforms baselines on the Tennessee Eastman process for industrial fault diagnosis.