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Multi-view generative synergistic optimization agent for few-shot machinery fault diagnosis under limited labeled data
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DOI：10.1177/14759217261430713.png)
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Abstract
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<jats:p>Mechanical fault diagnosis is crucial for maintaining the stability of industrial systems. However, the limited labeled fault data in real-world applications severely restricts diagnostic accuracy and generalization. To address this problem, this paper proposes a deep reinforcement learning–enhanced multi-view generative synergistic optimization agent, termed MGSOA, for machinery fault diagnosis under data-scarce conditions. First, a deep data sampling augmented generative adversarial network is designed to generate high-quality fault samples and mitigate data bias in few-shot scenarios. Furthermore, a multi-view feature extraction with domain adaptation architecture is constructed to adaptively extract multi-scale and multi-view features, enhancing feature representation under few-shot and mixed operating conditions. Finally, a novel priority-embedded reward-centering deep deterministic policy gradient algorithm is developed to improve the accuracy and robustness of the agent. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods in few-shot machinery fault diagnosis.</jats:p>
Keywords:
few-shot learning
machinery fault diagnosis
generative adversarial network
multi-view feature extraction
deep reinforcement learning
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